postgresql使用dblink跨庫增刪改查的步驟

Posted on 2023-04-02 by WalkonNet

postgresql使用dblink跨庫增刪改查

一、使用步驟

1、創建dblink擴展，連接與被連接的兩個數據庫都要執行下面sql

create extension if not exists dblink;

2、跨庫查詢或增刪改

#查詢
SELECT
	* 
FROM
	dblink ( 'host=localhost port=5432 dbname=piedssdb_sort user=postgres password=qQq314159@26', 'select user_id,account from piedss_biz.sys_user' ) AS T ( ID TEXT, NAME TEXT);
	
#新增
SELECT dblink_exec ( 'host=localhost port=5432 dbname=piedssdb_sort user=postgres password=qQq314159@26', 'INSERT INTO piedss_biz.sys_user(user_id,account,password,sex,super_admin_flag,status_flag,del_flag,create_user,real_name) VALUES(''1588006895019589631'',''ericfrq'',''1qazWSX'',''F'',''Y'',''1'',''N'',''dms_datahub'',''管理員'') ' );

#將庫A的數據查詢出來後直接插入存庫B
INSERT INTO dms_usercenter_userinfo ( ID, true_name, username, PASSWORD, phone ) SELECT
* 
FROM
	dblink ( 'host=localhost port=5432 dbname=piedssdb_sort user=postgres password=qQq314159@26', 'SELECT sys_user.user_id,sys_user.real_name,sys_user.account,sys_user.password,sys_user.tel FROM piedss_biz.sys_user' ) AS T ( ID TEXT, true_name TEXT, username TEXT, PASSWORD TEXT, phone TEXT );

#修改
SELECT dblink_exec ( 'host=localhost port=5432 dbname=piedssdb_sort user=postgres password=qQq314159@26', 'UPDATE piedss_biz.sys_user SET account=''ericfrq'',password=''1qazWSX'',sex=''F'',super_admin_flag=''Y'',status_flag=''1'',del_flag=''N'',create_user=''dms_datahub'',real_name=''管理員''WHERE  user_id=''158800689501958963111''' );

#刪除
SELECT dblink_exec ( 'host=localhost port=5432 dbname=piedssdb_sort user=postgres password=qQq314159@26', 'DELETE FROM piedss_biz.sys_user WHERE user_id=''4028db8283d486350183d533f7570000'' AND create_user=''dms_datahub''' );

3、如果不想每一次都寫完整的dblink連接信息，可以先起別名

#起別名
select dblink_connect('bieming', 'host=localhost port=5432 dbname=piedssdb_sort user=postgres password=qQq314159@26');
#進行操作
SELECT dblink_exec ( 'bieming', 'INSERT INTO piedss_biz.sys_user(user_id,account,password,sex,super_admin_flag,status_flag,del_flag,create_user,real_name) VALUES(''1588006895019589631'',''ericfrq'',''1qazWSX'',''F'',''Y'',''1'',''N'',''dms_datahub'',''管理員'') ' );
#關閉連接
SELECT dblink_disconnect('bieming');

4、補充：mybatis直接執行上面的sql寫法

參考下面補充介紹：pgsql個人筆記，mybatis+postgresql寫原生sql，不用xml

補充：pgsql個人筆記

一、mybatis+pgsql的xml

下面統計的sql中用到的聚合函數具體解析說明：第一部分

array_to_string( ARRAY_AGG ( stp.source_server ), ',' )：將stp的source_server的數據轉化為數組，再以逗號分隔拼接起來轉成字符串。
array_to_string( ARRAY_AGG ( stp.target_server ), ',' )將stp的target_server的數據轉化為數組，再將數組轉換為字符串，用“，”分隔。（有點類似於Mysql的group_concat()函數）
concat_ws ( ',', 'a', 'b' ) :將ab以逗號連接。在下面的案例中：concat_ws ( ',', array_to_string( ARRAY_AGG ( stp.source_server ), ',' ), array_to_string( ARRAY_AGG ( stp.target_server ), ',' ) )是將第一步的兩個結果，合並成一個字符串
regexp_split_to_table((a,b),',' ) :將a,b以逗號分隔開並將a、b分別作為表查詢的結果。在下面的案例中，regexp_split_to_table( ( concat_ws ( ',', array_to_string( ARRAY_AGG ( stp.source_server ), ',' ), array_to_string( ARRAY_AGG ( stp.target_server ), ',' ) ) ), ',' ) 將第二步的結果，以逗號“,”分隔,並把每一項作為結果
DISTINCT將第三步的結果去重
COUNT ( * )統計第四步去重後的數量

第二部分

SUM ( stad.data_volume )，計算data_volume的和
ROUND( '100' :: NUMERIC / 10, 3 )將100除以10後保留小數點後三位。其中:: NUMERIC將字符串’100’轉為數字(numeric類型最多能存儲有1000個數字位的數字並且能進行準確的數值計算。它主要用於需要準確地表示數字的場合，如貨幣金額。不過，對numeric 類型進行算術運算比整數類型和浮點類型要慢很多。)。案例中ROUND( SUM ( stad.data_volume ) :: NUMERIC / ( 1024 * 1024 * 1024 ), 3 )將第一步的結果轉為字符串並除以1024的三次方（將字節B轉–>kb–>mb–>GB）
CAST(oti.institution_id AS VARCHAR) 將int型institution_id轉為varchar型
將時間字段格式化為指定格式to_char(create_time,'yyyy-mm-dd')

mybatis+postgresql寫原生sql，不用xml

@Select({"${sqlStr}"})
    @Results({
            @Result(column = "gid", property = "gid", jdbcType = JdbcType.INTEGER, id = true),
            @Result(column = "name", property = "name", jdbcType = JdbcType.VARCHAR),
            @Result(column = "geom", property = "geom", jdbcType = JdbcType.VARCHAR),
            @Result(column = "code", property = "code", jdbcType = JdbcType.VARCHAR)
    })
    List<ModelPolygon> exeNativeSql(@Param("sqlStr") String sqlStr);



    @Select({"${sqlStr}"})
    List<ModelPolygon> exeNativeSql(@Param("sqlStr") String sqlStr);
    //"select gid as gid,name as name,ST_AsGeoJson(geom) as geom,code as code from wl_model_polygon"

整個dao層的寫法：

package com.xxx.mapper;

import com.alibaba.fastjson.JSONObject;
import com.baomidou.mybatisplus.core.mapper.BaseMapper;
import com.xxx.entity.UserInfo;
import org.apache.ibatis.annotations.*;
import org.apache.ibatis.type.JdbcType;

import java.util.List;

@Mapper
public interface UserInfoMapper extends BaseMapper<UserInfo> {

    @Select({"${sqlStr}"})
    @Results({
            @Result(column = "id", property = "id", jdbcType = JdbcType.VARCHAR, id = true),
            @Result(column = "username", property = "username", jdbcType = JdbcType.VARCHAR),
            @Result(column = "email", property = "email", jdbcType = JdbcType.VARCHAR),
            @Result(column = "phone", property = "phone", jdbcType = JdbcType.VARCHAR),
            @Result(column = "password", property = "password", jdbcType = JdbcType.VARCHAR),
            @Result(column = "true_name", property = "trueName", jdbcType = JdbcType.VARCHAR),
            @Result(column = "usetime", property = "usetime", jdbcType = JdbcType.VARCHAR)
    })
    List<UserInfo> exeNativeQuerySql(@Param("sqlStr") String sqlStr);

    @Select({"${sqlStr}"})
    List<JSONObject> exeNativeExecSql(@Param("sqlStr") String sqlStr);
}

 <!--數據量統計  -->
    <select id="getDataByParams" resultType="com.htht.datatrans.app.vo.CountProtocolVO">
		SELECT
			* 
		FROM
			(
			SELECT COUNT
				( * ) AS useNode 
			FROM
				(
				SELECT DISTINCT
					regexp_split_to_table(
						(
							concat_ws ( ',', array_to_string( ARRAY_AGG ( stp.source_server ), ',' ), array_to_string( ARRAY_AGG ( stp.target_server ), ',' ) ) 
						),
						',' 
					) 
				FROM
					sync_t_protocol AS stp 
				WHERE
					stp.deleted = 0 
					AND stp.protocol_type = 'data communication' 
				) res 
			) node1,
			(
			SELECT COUNT
				( * ) AS runningNode 
			FROM
				(
				SELECT DISTINCT
					regexp_split_to_table(
						(
							concat_ws ( ',', array_to_string( ARRAY_AGG ( stp.source_server ), ',' ), array_to_string( ARRAY_AGG ( stp.target_server ), ',' ) ) 
						),
						',' 
					) 
				FROM
					sync_t_protocol AS stp 
				WHERE
					stp.deleted = 0 
					AND stp.protocol_type = 'data communication' 
					AND stp.run_state = 'running' 
				) res 
			) node2,
			( SELECT COUNT ( * ) AS protocolTotal FROM sync_t_protocol AS stp WHERE stp.deleted = 0 AND stp.protocol_type = 'data communication' ) protocol1,
			(
			SELECT COUNT
				( * ) AS runningProtocol 
			FROM
				sync_t_protocol AS stp 
			WHERE
				stp.deleted = 0 
				AND stp.protocol_type = 'data communication' 
				AND stp.run_state = 'running' 
			) protocol2,
			(
			SELECT COUNT
				( * ) AS exceptionalProtocol 
			FROM
				sync_t_protocol AS stp 
			WHERE
				stp.deleted = 0 
				AND stp.protocol_type = 'data communication' 
				AND stp.run_state = 'exception' 
			) protocol3,
			(
			SELECT
				ROUND( SUM ( stad.data_volume ) :: NUMERIC / ( 1024 * 1024 * 1024 ), 3 ) AS runningData 
			FROM
				sync_t_action_detail AS stad
				INNER JOIN sync_t_protocol AS stp ON stad.protocol_id = stp.protocol_id 
				AND stp.deleted = 0 
				AND protocol_type = 'data communication' 
			WHERE
				stad.execute_state = ANY ( STRING_TO_ARRAY( 'running', ',' ) ) 
			) data1,
			(
			SELECT
				ROUND( SUM ( stad.data_volume ) :: NUMERIC / ( 1024 * 1024 * 1024 ), 3 ) AS historyData 
			FROM
				sync_t_action_detail AS stad
				INNER JOIN sync_t_protocol AS stp ON stad.protocol_id = stp.protocol_id 
				AND stp.deleted = 0 
				AND protocol_type = 'data communication' 
			WHERE
			stad.execute_state = ANY ( STRING_TO_ARRAY( 'succeed,failed', ',' ) ) 
			) data2
	</select>
	
<select
		id="getPagesByParams"
		resultType="com.htht.datatrans.app.vo.CloudVO">
		select * from ops_t_cloud where delete=0
		<if test="cloudProvider != null and cloudProvider != ''">
			and cloud_provider like '%'||#{cloudProvider,jdbcType=VARCHAR}||'%'
		</if>
		order by cloud_id
	</select>

<select
		id="getByCloudCodes"
		resultType="com.htht.datatrans.app.entity.Cloud">
		select * from ops_t_cloud where delete=0
		<if test="cloudCodes != null and cloudCodes != ''">
			and cloud_code = ANY(STRING_TO_ARRAY(#{cloudCodes,jdbcType=VARCHAR}, ','))
		</if>
		order by cloud_id
	</select>
	<select
		id="getPagesByParams"
		resultType="org.springblade.modules.datatrans.vo.ServerPageVO">
		select ots.*,otc.cloud_name as cloudName,otc.domain_name as domainName 
		from ops_t_server ots 
		inner join ops_t_cloud otc on ots.cloud_id = otc.cloud_id 
		<if test="institutionId != null and institutionId != ''">
			inner join ops_t_institution oti ON CAST(oti.institution_id AS VARCHAR) = ots.institution_id
		</if>
		where ots.deleted=0 
		<if test="cloudProvider != null and cloudProvider != ''">
			and otc.cloud_provider like concat(concat('%',#{cloudProvider,jdbcType=VARCHAR}),'%')
		</if>
		order by ots.server_id
	</select>

二、字符串替換

將address字段裡的 “區” 替換為 “嘔” 顯示，如下

select *,replace(address,'區','嘔') AS rep
from test_tb

將name字段裡的 “我” 替換為 “你” 保存，如下

UPDATE blade_visual 
SET "name" = ( REPLACE ( NAME, '你', '你們三' ) )

三、postgre做空間數據分析

比如面相交

1、使用步驟新建空間索引create extension postgis;創建geometry類型字段

3.插入geometry數據

insert into wl_model_polygon(geom,name,code) values ('SRID=4326;POLYGON ((116.2078857421875 39.928694653732364, 116.20925903320312 39.91078961774283, 116.20651245117188 39.89393354266699, 116.23397827148436 39.86547951378614, 116.24496459960938 39.82752244475985, 116.29852294921876 39.78954439311165, 116.3397216796875 39.78532331459258, 116.3836669921875 39.78848914776114, 116.41799926757811 39.79904087286648, 116.444091796875 39.80748108746673, 116.45919799804688 39.818029898770206, 116.48117065429686 39.83490462943255, 116.50314331054688 39.86231722624386, 116.50588989257812 39.88023492849342, 116.5045166015625 39.90973623453719, 116.4935302734375 39.925535281697286, 116.5045166015625 39.94975340768179, 116.47979736328125 39.98132938627215, 116.47567749023438 39.99395569397331, 116.45507812500001 40.000267972646796, 116.43859863281249 40.000267972646796, 116.4166259765625 39.998163944585805, 116.36581420898438 40.00868343656941, 116.35208129882812 40.00447583427404, 116.30264282226562 40.01078714046552, 116.27792358398436 39.999215966720165, 116.24771118164061 39.99500778093748, 116.23260498046874 39.990799335838034, 116.21200561523438 39.95606977009003, 116.2078857421875 39.928694653732364))
','產流區單元','1');

insert into wl_model_polygon(geom,name) values ('SRID=4326;POLYGON ((118.76382985390228 30.94145000894207, 118.76367454479498 30.941584547525736, 118.76350796485406 30.941783659824637, 118.76339844820404 30.941924731032316, 118.76330916107543 30.942036894992782, 118.76327040751187 30.94208876002824, 118.76320401397413 30.942103072784164, 118.76311833308432 30.942151844969032, 118.76297412628924 30.94233241273298, 118.76284033474406 30.942507490217793, 118.76274061465483 30.942508998759877, 118.76272709824036 30.942414705157432, 118.76260312963427 30.941400575247428, 118.76246246134042 30.940958834692708, 118.76241983918237 30.940824987759868, 118.76235477020532 30.94068130925791, 118.76232222882629 30.940647540114867, 118.76293788696353 30.940087796711964, 118.76307156743417 30.939971500356137, 118.76327063857775 30.93979831612114, 118.7635558539929 30.939541452438277, 118.7637265129556 30.93939848398361, 118.76377770256443 30.939355600092142, 118.76441910672565 30.9388159785355, 118.76463064154075 30.938667159236218, 118.76495341070222 30.938493604345012, 118.76523672506141 30.938409477348614, 118.7654197381786 30.9383707434975, 118.76582985307277 30.938323591604444, 118.76622053407164 30.9382963001612, 118.76643330279228 30.938318107809664, 118.7664801815057 30.938337017341382, 118.76652477352764 30.938350675989682, 118.7666582796586 30.938456597505137, 118.76673673369658 30.938603248874927, 118.76677236100761 30.938782266531803, 118.76684549711081 30.939149764149192, 118.76701632885761 30.93988929949859, 118.7670376347395 30.939981532336844, 118.7664187768753 30.94010020307178, 118.76614981686157 30.940150404326346, 118.7658940991671 30.940243370814187, 118.76569247579346 30.940342755588517, 118.76556089310861 30.940412552128976, 118.76552036966268 30.940466789099446, 118.76550573912039 30.940574355758315, 118.76551217968313 30.941150469586262, 118.76551098575817 30.941290908017095, 118.76550989936004 30.941418699044846, 118.76542260756776 30.94141695016964, 118.76499121731501 30.941408306476433, 118.76391937007008 30.94138581330907, 118.76382985390228 30.94145000894207))','產流區單元');

4.pg庫清空數據和主鍵自增

TRUNCATE TABLE wl_model_polygon;

TRUNCATE wl_model_polygon RESTART IDENTITY;

5.相交分析sql

select gid,name,ST_AsGeoJson(geom) as geom from wl_model_polygon t where ST_Intersects(t.geom,ST_GeomFromGeoJSON('{"type":"Polygon","coordinates":[[[118.78355107920095,30.938155072659868],[118.78134774048146,30.939763084116294],[118.7812957819458,30.93972376187253],[118.78286595934765,30.93838280705404],[118.7833158576293,30.93793078253492],[118.78355013577584,30.938153972966006],[118.78355107920095,30.938155072659868]]]}
'))

select gid,name,ST_AsGeoJson(geom) as geom from wl_model_polygon t where ST_Intersects(t.geom,ST_GeomFromGeoJSON('{"type":"Polygon","coordinates":[[[118.78355107920095,30.938155072659868],[118.78134774048146,30.939763084116294],[118.7812957819458,30.93972376187253],[118.78286595934765,30.93838280705404],[118.7833158576293,30.93793078253492],[118.78355013577584,30.938153972966006],[118.78355107920095,30.938155072659868]]]}'))

四、自增序列

1、navicat創建自增字段

設置為serial4類型

保存後自動加序列

2、重置自增序列號為指定數值

第一步：select pg_get_serial_sequence('ts_mapservice', 'f_remark');查看序列為public.ts_mapservice_f_remark_seq
第二步：更新序列值ALTER SEQUENCE public.ts_mapservice_f_remark_seq RESTART WITH 8;
或者直接初始化自增數值：TRUNCATE TABLE wl_model_polygon; TRUNCATE wl_model_polygon RESTART IDENTITY;

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postgresql使用dblink跨庫增刪改查的步驟

目錄