别裁员!别裁员!别裁员! 一招降低IT数据库成本__2020疫情无情，诸多企业因此受挫，特别中小企业，甚至到了要裁员的地步, 但是人才是最宝贵的，裁员一定是下下策，如何渡过这个难关，疫情带给我们什么反思? 开源节流有新方法，通常数据库在企业IT支出中的占比将近一半，降低数据库成本对降低企业IT成本效果明显，但是一般企业没有专业DBA，很难在这方面下手，不过没关系，有了云厂商，一切变得简单。借助阿里云我们找到了可以为企业IT节省至少一倍成本的方法.: 世界上功能最强大的开源数据库...

### **背景**
#### 2020疫情无情，诸多企业因此受挫，特别中小企业，甚至到了要裁员的地步, 但是人才是最宝贵的，裁员一定是下下策，如何渡过这个难关，疫情带给我们什么反思?
#### 开源节流有新方法，通常数据库在企业IT支出中的占比将近一半，降低数据库成本对降低企业IT成本效果明显，但是一般企业没有专业DBA，很难在这方面下手，不过没关系，有了云厂商，一切变得简单。借助阿里云我们找到了可以为企业IT节省至少一倍成本的方法.
#### 到底时什么方法呢? 回顾一下年前做的一系列MySQL+PG联合解决方案的课程.
#### 《阿里云 RDS PostgreSQL+MySQL 联合解决方案课程 - 汇总视频、课件》
#### 在众多数据库中, PG是一个企业级的开源数据库, 各方面的功能与Oracle对齐, 适合范围广, 能处理的数据量庞大. 采用PG的大型企业例如平安，邮储银行，阿里，华为，中兴，人保, 招商, 富士康, 苹果, SAP, saleforce等以及全球财富1000强等众多企业。 《外界对PostgreSQL 的评价》
#### 阿里云RDS PG的优势:

#### · 支持完整生命周期管理,包括高可用, 容灾, 备份, 安全, 审计, 加密, cloud dba等模块, 大幅降低企业的使用和管理成本.
#### · 专业内核和DBA团队 7*24小时服务.
#### · 支持并行计算，LLVM，GPU加速，向量计算，分析能力更强。
#### · PG的优化器强大，应对复杂SQL处理效率更高，适合复杂业务场景, 更适合新零售、制造业、工业、在线教育、游戏、金融、政府、企业ERP等行业或领域。
#### · 内核扩展, 根据垂直领域的需求定制化。
#### o Ganos插件, GIS功能更强更专业，支持平面、球面几何，栅格，时空轨迹，点云，拓扑网络模型。
#### o pase插件, 支持高维向量搜索, 支持精确的图像搜索, 人脸识别, 相似查询.
#### o roaringbitmap插件, 支持实时大数据用户画像, 精准营销.
#### o rdkit插件, 支持化学分析, 分子式的相似搜索, 化学机器学习等.
#### 多模能力更强，其表现在索引更丰富，除了btree,hash还支持gin,gist,spgist,brin,bloom,rum等索引接口，适合模糊搜索，全文检索，多维任意搜索，时空搜索，高维向量（广泛应用于图像识别、相似特征扩选，时序搜索，用户画像，化学分析，DNA检索等。
#### 类型更加丰富，同时支持扩展类型，除了基本类型以外，支持网络、全文检索、数组、xml、JSON、范围、域、树、多维、分子、GIS等类型。支持更丰富的应用场景。
#### · 支持oss_fdw, 可以将数据库的归档数据存储在oss中, 降低成本, 并且访问方法不变.
#### 本文将对PG和MySQL进行多方位对比, 在某些方面PG的综合性能比MySQL高出一个数量级, PG+MySQL结合使用, 可以大幅降低企业成本.
#### 疫情无情PG有情, 别裁员了, 建立多元化的技术栈, 强化企业IT能力更重要.
### **环境**
#### 阿里云RDS PG 12实例, 8核32G 1500G ESSD
#### 同硬件配置的MySQL 8.0
#### 用户密码:
#### user123      
#### xxxxxxxx!      
#### 库:
#### db1      
#### 连接串:
#### PG:
```
export PGPASSWORD=xxxxxxxx!      
psql -h pgm-bp1z26gbo3gx893a129310.pg.rds.aliyuncs.com -p 1433 -U user123 db1      
MySQL:
mysql -h rm-bp1wv992ym962k85888370.mysql.rds.aliyuncs.com -P 3306 -u user123 --password=xxxxxx! -D db1
```
#### 测试用的客户端ecs centos 7.x x64安装mysql, pg客户端
```
yum install -y mysql-*      
yum install https://download.postgresql.org/pub/repos/yum/reporpms/EL-7-x86_64/pgdg-redhat-repo-latest.noarch.rpm      
yum install -y postgresql12
```
### **MySQL 8.0测试**
#### 测试表
```
CREATE TABLE employees (      
id INT NOT NULL,      
  fname VARCHAR(30),      
  lname VARCHAR(30),      
  birth TIMESTAMP,      
  hired DATE NOT NULL DEFAULT '1970-01-01',      
  separated DATE NOT NULL DEFAULT '9999-12-31',      
  job_code INT NOT NULL,      
  store_id INT NOT NULL
);
```
#### 批量写入存储过程
```
DROP PROCEDURE IF EXISTS BatchInsert;

delimiter //   -- 把界定符改成双斜杠      
CREATE PROCEDURE BatchInsert(IN init INT, IN loop_time INT)  -- 第一个参数为初始ID号（可自定义），第二个位生成MySQL记录个数      
  BEGIN      
DECLARE Var INT;      
DECLARE ID INT;      
      SET Var = 0;      
      SET ID = init;      
WHILE Var < loop_time DO
          insert into employees      
          (id, fname, lname, birth, hired, separated, job_code, store_id)       
          values       
          (ID, CONCAT('chen', ID), CONCAT('haixiang', ID), Now(), Now(), Now(), 1, ID);      
          SET ID = ID + 1;      
          SET Var = Var + 1;      
      END WHILE;      
  END;      
//      
delimiter ;  -- 界定符改回分号
```
#### 批量写入20万条
```
-- 开启事务插入,否则会很慢      
      
begin;      
CALL BatchInsert(1, 200000);      
commit;      
      
Query OK, 1 row affected (7.53 sec) 
```
   
#### 使用insert into继续批量写入
```
mysql> insert into employees select * from employees;      
Query OK, 200000 rows affected (1.61 sec)      
Records: 200000  Duplicates: 0  Warnings: 0      
      
mysql> insert into employees select * from employees;      
Query OK, 400000 rows affected (3.25 sec)      
Records: 400000  Duplicates: 0  Warnings: 0      
      
mysql> insert into employees select * from employees;      
Query OK, 800000 rows affected (6.51 sec)      
Records: 800000  Duplicates: 0  Warnings: 0      
      
mysql> insert into employees select * from employees;      
Query OK, 1600000 rows affected (12.93 sec)      
Records: 1600000  Duplicates: 0  Warnings: 0      
      
mysql> insert into employees select * from employees;      
Query OK, 3200000 rows affected (28.61 sec)      
Records: 3200000  Duplicates: 0  Warnings: 0      
      
mysql> insert into employees select * from employees;      
Query OK, 6400000 rows affected (56.48 sec)      
Records: 6400000  Duplicates: 0  Warnings: 0      
      
mysql> insert into employees select * from employees;      
Query OK, 12800000 rows affected (1 min 55.30 sec)      
Records: 12800000  Duplicates: 0  Warnings: 0  
```    
#### 查询性能
```
mysql> select count(*) from employees;      
+----------+      
| count(*) |      
+----------+      
| 25600000 |      
+----------+      
1 row in set (6.15 sec)     
``` 
#### 求distinct性能
```
mysql> select count(distinct id) from employees ;      
+--------------------+      
| count(distinct id) |      
+--------------------+      
|             200000 |      
+--------------------+      
1 row in set (16.67 sec)
```

#### 分组求distinct性能
```
mysql> select count(*) from (select id from employees group by id) t;      
+----------+      
| count(*) |      
+----------+      
|   200000 |      
+----------+      
1 row in set (15.52 sec)
```
#### 再写入200万
```
begin;      
CALL BatchInsert(1, 2000000);      
commit;
```
#### 测试表2, 写入200万.
```
CREATE TABLE employees1 (      
  id INT NOT NULL,      
  fname VARCHAR(30),      
  lname VARCHAR(30),      
  birth TIMESTAMP,      
  hired DATE NOT NULL DEFAULT '1970-01-01',      
  separated DATE NOT NULL DEFAULT '9999-12-31',      
  job_code INT NOT NULL,      
  store_id INT NOT NULL
);

DROP PROCEDURE IF EXISTS BatchInser1;

delimiter //   -- 把界定符改成双斜杠      
CREATE PROCEDURE BatchInsert1(IN init INT, IN loop_time INT)  -- 第一个参数为初始ID号（可自定义），第二个位生成MySQL记录个数      
  BEGIN      
DECLARE Var INT;      
DECLARE ID INT;      
      SET Var = 0;      
      SET ID = init;      
WHILE Var < loop_time DO
          insert into employees1      
          (id, fname, lname, birth, hired, separated, job_code, store_id)       
          values       
          (ID, CONCAT('chen', ID), CONCAT('haixiang', ID), Now(), Now(), Now(), 1, ID);      
          SET ID = ID + 1;      
          SET Var = Var + 1;      
      END WHILE;      
  END;      
//      
delimiter ;  -- 界定符改回分号
```
#### 使用loop insert写入200万行
```

-- 开启事务插入,否则会很慢

begin;      
CALL BatchInsert1(1, 2000000);      
commit;

Query OK, 1 row affected (1 min 7.06 sec)
```
#### 2560万 多对一JOIN 200万, 分组,排序
```
select t1.lname,count(*) from employees t1 join employees1 t2 using (id) group by t1.lname order by count(*) desc,lname limit 10;
```
#### 简单查询性能(因为以上查询几个小时都没有出结果, 不得不新建一个200万的表进行查询测试):
```
CREATE TABLE employees2 (      
id INT NOT NULL,      
fname VARCHAR(30),      
lname VARCHAR(30),      
birth TIMESTAMP,      
hired DATE NOT NULL DEFAULT '1970-01-01',      
separated DATE NOT NULL DEFAULT '9999-12-31',      
job_code INT NOT NULL,      
store_id INT NOT NULL      
);

DROP PROCEDURE IF EXISTS BatchInser2;

delimiter //   -- 把界定符改成双斜杠      
CREATE PROCEDURE BatchInsert2(IN init INT, IN loop_time INT)  -- 第一个参数为初始ID号（可自定义），第二个位生成MySQL记录个数      
BEGIN      
DECLARE Var INT;      
DECLARE ID INT;      
SET Var = 0;      
SET ID = init;      
WHILE Var < loop_time DO      
insert into employees2      
(id, fname, lname, birth, hired, separated, job_code, store_id)       
values       
(ID, CONCAT('chen', ID), CONCAT('haixiang', ID), Now(), Now(), Now(), 1, ID);      
SET ID = ID + 1;      
SET Var = Var + 1;      
END WHILE;      
END;      
//      
delimiter ;  -- 界定符改回分号

-- 开启事务插入,否则会很慢

begin;      
CALL BatchInsert2(1, 2000000);      
commit;

Query OK, 1 row affected (1 min 7.06 sec)
```
#### 创建索引
```
create index idx_employees2_1 on employees2(id);
```
#### 建立查询存储过程, 查询200万次.
```
DROP PROCEDURE IF EXISTS select1;

delimiter //   -- 把界定符改成双斜杠      
CREATE PROCEDURE select1(IN init INT, IN loop_time INT)  -- 第一个参数为初始ID号（可自定义），第二个位生成MySQL记录个数      
BEGIN      
DECLARE Var INT;      
DECLARE ID1 INT;      
DECLARE vid INT;      
DECLARE vfname VARCHAR(30);      
DECLARE vlname VARCHAR(30);      
DECLARE vbirth TIMESTAMP;      
DECLARE vhired DATE;      
DECLARE vseparated DATE;      
DECLARE vjob_code INT;      
DECLARE vstore_id INT;      
SET Var = 0;      
SET ID1 = init;      
WHILE Var < loop_time DO      
select t.id,t.fname,t.lname,t.birth,t.hired,t.separated,t.job_code,t.store_id       
into       
vid,vfname,vlname,vbirth,vhired,vseparated,vjob_code,vstore_id      
from employees2 t       
where t.id=id1;      
SET ID1 = ID1 + 1;      
SET Var = Var + 1;      
END WHILE;      
END;      
//      
delimiter ;  -- 界定符改回分号
```
#### 基于KEY简单查询, 查询200万次的耗时.
```
-- 开启事务查询

begin;      
CALL select1(1, 2000000);      
commit;

Query OK, 1 row affected (1 min 10.23 sec)
```
### **MySQL 1亿+:**
#### 继续测试到1亿数据量.
```
mysql> insert into employees select * from employees;    Query OK, 27600000 rows affected (4 min 38.62 sec)    Records: 27600000  Duplicates: 0  Warnings: 0    mysql> insert into employees select * from employees;    Query OK, 55200000 rows affected (11 min 13.40 sec)    Records: 55200000  Duplicates: 0  Warnings: 0    mysql> select count(*) from employees;    +-----------+    | count(*)  |    +-----------+    | 110400000 |    +-----------+    1 row in set (28.00 sec)    mysql> select count(distinct id) from employees ;      +--------------------+    | count(distinct id) |    +--------------------+    |            2000000 |    +--------------------+    1 row in set (1 min 17.73 sec)    mysql> select count(*) from (select id from employees group by id) t;      +----------+    | count(*) |    +----------+    |  2000000 |    +----------+    1 row in set (1 min 24.64 sec)
```
#### 1.1亿全量数据更新
```
mysql> update employees set lname=lname||'new';  
Query OK, 110400000 rows affected, 65535 warnings (21 min 30.34 sec)  
Rows matched: 110400000  Changed: 110400000  Warnings: 220800000
```
#### 1.1亿 多对一JOIN 200万, 分组,排序, 超过3小时没有查询出结果.
```
select t1.lname,count(*) from employees t1 join employees1 t2 using (id) group by t1.lname order by count(*) desc,lname limit 10;   
```
#### 1.1亿创建索引
```
mysql> create index idx_employees_1 on employees(id);    
Query OK, 0 rows affected (3 min 49.04 sec)    
Records: 0  Duplicates: 0  Warnings: 0
```
### **阿里云RDS PostgreSQL 12测试**
#### 测试表
```
CREATE TABLE employees (      
id INT NOT NULL,      
  fname VARCHAR(30),      
  lname VARCHAR(30),      
  birth TIMESTAMP,      
  hired DATE NOT NULL DEFAULT '1970-01-01',      
  separated DATE NOT NULL DEFAULT '9999-12-31',      
  job_code INT NOT NULL,      
  store_id INT NOT NULL
);
```
#### 直接使用srf快速写入20万数据
```
\timing

insert into employees      
    (id, fname, lname, birth, hired, separated, job_code, store_id)       
select
ID, CONCAT('chen', ID), CONCAT('haixiang', ID), Now(), Now(), Now(), 1, ID
from generate_series(1,200000) id;

INSERT 0 200000
Time: 355.652 ms
```
#### 也可以使用和mysql一样的方法loop insert写入20万
```
create or replace function BatchInsert(IN init INT, IN loop_time INT)  -- 第一个参数为初始ID号（可自定义），第二个位生成记录个数      
returns void as $$      
DECLARE
Var INT := 0;      
begin
for id in init..init+loop_time-1 loop
insert into employees      
    (id, fname, lname, birth, hired, separated, job_code, store_id)       
values
    (ID, CONCAT('chen', ID), CONCAT('haixiang', ID), Now(), Now(), Now(), 1, ID);      
end loop;      
end;      
$$ language plpgsql strict;

db1=# select batchinsert(1,200000);      
 batchinsert       
-------------

(1 row)      
Time: 1292.559 ms (00:01.293)
```
#### 使用insert into继续批量写入
```
db1=> insert into employees select * from employees ;      
INSERT 0 400000      
Time: 322.335 ms      
db1=> insert into employees select * from employees ;      
INSERT 0 800000      
Time: 835.365 ms      
db1=> insert into employees select * from employees ;      
INSERT 0 1600000      
Time: 1622.475 ms (00:01.622)      
db1=> insert into employees select * from employees ;      
INSERT 0 3200000      
Time: 3583.787 ms (00:03.584)      
db1=> insert into employees select * from employees ;      
INSERT 0 6400000      
Time: 7277.764 ms (00:07.278)      
db1=> insert into employees select * from employees ;      
INSERT 0 12800000      
Time: 15639.482 ms (00:15.639)      
db1=> \dt+ employees       
List of relations      
Schema |   Name    | Type  |  Owner  |  Size   | Description       
--------+-----------+-------+---------+---------+-------------      
public | employees | table | user123 | 2061 MB |       
(1 row)
```
#### 查询性能
```
db1=> select count(*) from employees ;      
count         
----------      
25600000      
(1 row)

Time: 604.982 ms
```
#### 求distinct性能
```
db1=> select count(*) from (select id from employees group by id) t;      
 count        
--------      
200000
(1 row)

Time: 2982.907 ms (00:02.983)
```
#### 再写入200万
```
insert into employees      
    (id, fname, lname, birth, hired, separated, job_code, store_id)       
select
ID, CONCAT('chen', ID), CONCAT('haixiang', ID), Now(), Now(), Now(), 1, ID
from generate_series(1,2000000) id;
```
#### 测试表2, 写入200万.
```
CREATE TABLE employees1 (      
id INT NOT NULL,      
  fname VARCHAR(30),      
  lname VARCHAR(30),      
  birth TIMESTAMP,      
  hired DATE NOT NULL DEFAULT '1970-01-01',      
  separated DATE NOT NULL DEFAULT '9999-12-31',      
  job_code INT NOT NULL,      
  store_id INT NOT NULL
);

insert into employees1      
    (id, fname, lname, birth, hired, separated, job_code, store_id)       
select
ID, CONCAT('chen', ID), CONCAT('haixiang', ID), Now(), Now(), Now(), 1, ID
from generate_series(1,2000000) id;

INSERT 0 2000000
Time: 3037.777 ms (00:03.038)
```
#### 2560万 多对一JOIN 200万, 分组,排序
```
select t1.lname,count(*) from employees t1 join employees1 t2 using (id) group by t1.lname order by count(*) desc,lname limit 10;      
     lname      | count       
----------------+-------      
 haixiang1      |   129      
 haixiang10     |   129      
 haixiang100    |   129      
 haixiang1000   |   129      
 haixiang10000  |   129      
 haixiang100000 |   129      
 haixiang100001 |   129      
 haixiang100002 |   129      
 haixiang100003 |   129      
 haixiang100004 |   129      
(10 rows)

Time: 8897.907 ms (00:08.898)
```
#### 简单查询性能:
#### 创建索引
```
create index idx_employees1_1 on employees1(id);      
CREATE INDEX
Time: 1436.346 ms (00:01.436)
```
#### 基于KEY简单查询, 查询200万次的耗时.
```
do language plpgsql $$       
declare
begin
for i in 1..2000000 loop
    perform * from employees1 where id=i;      
end loop;      
end;      
$$;

DO
Time: 9515.728 ms (00:09.516)      
db1=> select 9515.728/2000000;      
        ?column?              
------------------------      
 0.00475786400000000000      
(1 row)
```
### **PG 1亿+:** 
```
db1=> INSERT INTO employees select * from employees;      
INSERT 0 27600000      
Time: 25050.665 ms (00:25.051)

db1=> INSERT INTO employees select * from employees;      
INSERT 0 55200000      
Time: 64726.430 ms (01:04.726)      
db1=> select count(*) from employees;      
count         
-----------      
110400000      
(1 row)

Time: 7286.152 ms (00:07.286)      
db1=> select count(distinct id) from employees;      
count        
---------      
2000000      
(1 row)

Time: 39783.068 ms (00:39.783)      
db1=> select count(*) from (select id from employees group by id) t;      
count        
---------      
2000000      
(1 row)

Time: 33731.431 ms (00:33.731)
```
#### 更新1.1亿
```
db1=> update employees set lname=lname||'new';  
UPDATE 110400000  
Time: 385372.063 ms (06:25.372)
```
#### 创建索引:
```
db1=> create index idx_employees_1 on employees(id);    
CREATE INDEX    
Time: 70450.491 ms (01:10.450)
```
### **MySQL vs PG 性能报表**
#### 8核32G 1500G essd云盘， MySQL 8.0 vs PG 12

| 数据量 | sql |  MySQL耗时 | PG耗时 | PG vs MySQL性能倍数
| :------:|  :------:| :------: | :------: | :------: |
| 20万  {写入} | 存储过程loop | insert | 7.53 s | 1.29 s | 5.84 |
| 20万  {写入} | SRF insert | 不支持 | 0.36 s | - |
| 40万  {写入} | INSERT INTO employees select * from employees; | 3.25 s | 0.32 s | 10.16 |
| 80万  {写入} | INSERT INTO employees select * from employees; | 6.51 s | 0.84 s | 7.75 |
| 160万  {写入} | INSERT INTO employees select * from employees; | 12.93 s | 1.62 s | 7.95 |
| 320万  {写入} | INSERT INTO employees select * from employees; | 28.61 s | 3.58 s | 7.99 |
| 640万  {写入} | INSERT INTO employees select * from employees; | 56.48 s | 7.28 s | 7.76 |
| 1280万  {写入} | INSERT INTO employees select * from employees; | 115.30 s | 15.64 s | 7.37 |
| 2760万  {写入} | INSERT INTO employees select * from employees; | 278.62 s | 25.05 s | 11.12 |
| 5520万  {写入} | INSERT INTO employees select * from employees; | 673.40 s | 64.73 s | 10.40 |
| 200万  {普通查询} | KV查询200万次.PS: 进程模型,建议实际应用时使用连接池，总连接控制在1000以内绝佳,未来支持内置线程池,几万连接完全没问题. | 70.23 s | 9.52 s | 7.38 |
| 2560万  {复杂查询} | select count(*) from employees; | 6.15 s | 0.60 s | 10.25 |
| 2560万  {复杂查询} | select count(distinct id) from employees; | 16.67 s | 7.85 s | 2.12 |
| 2560万  {复杂查询} | select count(*) from (select id from employees group by id) t; | 15.52 s | 2.98 s | 5.21 |
| 1.1亿  {复杂查询} | select count(*) from employees; | 28 s | 7.29 s | 3.84 |
| 1.1亿  {复杂查询} | select count(distinct id) from employees; | 77.73 s | 39.78 s | 1.95 |
| 1.1亿  {复杂查询} | select count(*) from (select id from employees group by id) t; | 84.64 s | 14.67 s | 5.77 |
| 2760万 多对一JOIN 200万  {JOIN + 运算} | select t1.lname,count(*) from employees t1 join employees1 t2 using (id) group by t1.lname order by count(*) desc,lname limit 10; | 超过3小时未出结果 | 8.90 s | 至少 1213.48 |
| 1.1亿 多对一JOIN 200万  {JOIN + 运算} | select t1.lname,count(*) from employees t1 join employees1 t2 using (id) group by t1.lname order by count(*) desc,lname limit 10;  | 超过3小时未出结果 | 33.73 s | 至少 320.19 |
| 1.1亿  {更新} | update employees set lname=concat(lname,'new'); | 1290.34 s | 70.45 s | 18.32 |
| 1.1亿  {创建索引} | create index idx_employees_1 on employees(id); | 229.04 s | 70.45 s | 3.25 |
 
#### 通过以上测试, 在大多数场景中, 阿里云RDS PG相比MySQL的综合性能提升了1个数量级, PG+MySQL结合使用可以大幅降低企业成本. 疫情无情PG有情, 别裁员了, 建立多元化的技术栈, 强化企业IT能力更重要.
#### 更多应用场景和使用方法请参考回顾视频, 包括如何将mysql数据同步到pg(dts):
#### 《阿里云 RDS PostgreSQL+MySQL 联合解决方案课程 - 汇总视频、课件》
#### 课程内容:

#### · 2019.12.30 19:30 RDS PG产品概览，如何与mysql结合使用
#### · 2019.12.31 19:30 如何连接PG，GUI(pgadmin, navicat, dms)，cli的使用
#### · 2020.1.3 19:30 如何压测PG数据库、如何瞬间构造海量测试数据
#### · 2020.1.6 19:30 mysql与pg类型、语法、函数等对应关系
####  2020.1.7 19:30 如何将mysql数据同步到pg（dts）
#### · 2020.1.8 19:30 PG外部表妙用 - mysql_fdw, oss_fdw（直接读写mysql、冷热分离）
#### · 2020.1.9 19:30 PG应用场景介绍 - 并行计算，实时分析
#### · 2020.1.10 19:30 PG应用场景介绍 - GIS
#### · 2020.1.13 19:30 PG应用场景介绍 - 用户画像、实时营销系统
#### · 2020.1.14 19:30 PG应用场景介绍 - 多维搜索
#### · 2020.1.15 19:30 PG应用场景介绍 - 向量计算、图像搜索
#### · 2020.1.16 19:30 PG应用场景介绍 - 全文检索、模糊查询
#### · 2020.1.17 19:30 pg 数据分析语法介绍
#### · 2020.1.18 19:30 pg 更多功能了解：扩展语法、索引、类型、存储过程与函数。如何加入PG技术社群
#### 阿里云PG免费试用活动进行中, 请钉钉扫码加入咨询:
 
![CENTER_PostgreSQL_Community]( /images/news/2020/20200218_2107_图片1.png)

#### 文中的相关链接:
 
#### 1、《阿里云 RDS PostgreSQL+MySQL 联合解决方案课程 - 汇总视频、课件》
https://github.com/digoal/blog/blob/master/202001/20200118_02.md
 
#### 2、https://github.com/digoal/blog/blob/master/202002/20200206_02.md
https://github.com/digoal/blog/blob/master/202002/20200206_02.md
 
#### 3、《PG buildin pool(内置连接池)版本 原理与测试》
https://github.com/digoal/blog/blob/master/201805/20180521_03.md

![CENTER_PostgreSQL_Community](/images/news/2016/pg_bot_banner.jpg)