postgresql使用dblink跨庫增刪改查的步驟
更新時間：2023年04月01日 11:25:01 作者：EricFRQ
這篇文章主要介紹了postgresql使用dblink跨庫增刪改查，本文給大家介紹的非常詳細(xì)對大家的學(xué)習(xí)或工作具有一定的參考借鑒價值,需要的朋友可以參考下
postgresql使用dblink跨庫增刪改查
一、使用步驟
1、創(chuàng)建dblink擴展，連接與被連接的兩個數(shù)據(jù)庫都要執(zhí)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(shù)據(jù)查詢出來后直接插入存庫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(guān)閉連接
SELECT dblink_disconnect('bieming');4、補充：mybatis直接執(zhí)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(tǒng)計的sql中用到的聚合函數(shù)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(shù)據(jù)轉(zhuǎn)化為數(shù)組，再以逗號分隔拼接起來轉(zhuǎn)成字符串。
- array_to_string( ARRAY_AGG ( stp.target_server ), ',' )將stp的target_server的數(shù)據(jù)轉(zhuǎn)化為數(shù)組，再將數(shù)組轉(zhuǎn)換為字符串，用“，”分隔。（有點類似于Mysql的group_concat()函數(shù)）
- 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(jié)果，合并成一個字符串
- regexp_split_to_table((a,b),',' ) :將a,b以逗號分隔開并將a、b分別作為表查詢的結(jié)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(jié)果，以逗號“,”分隔,并把每一項作為結(jié)果
- DISTINCT將第三步的結(jié)果去重
- COUNT ( * )統(tǒng)計第四步去重后的數(shù)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后保留小數(shù)點后三位。其中:: NUMERIC將字符串’100’轉(zhuǎn)為數(shù)字(numeric類型最多能存儲有1000個數(shù)字位的數(shù)字并且能進行準(zhǔn)確的數(shù)值計算。它主要用于需要準(zhǔn)確地表示數(shù)字的場合，如貨幣金額。不過，對numeric 類型進行算術(shù)運算比整數(shù)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(jié)果轉(zhuǎn)為字符串并除以1024的三次方（將字節(jié)B轉(zhuǎn)–>kb–>mb–>GB）
- CAST(oti.institution_id AS VARCHAR) 將int型institution_id轉(zhuǎn)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(shù)據(jù)量統(tǒng)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字段里的 “區(qū)” 替換為 “嘔” 顯示，如下
select *,replace(address,'區(qū)','嘔') AS rep from test_tb
將name字段里的 “我” 替換為 “你” 保存，如下
UPDATE blade_visual SET "name" = ( REPLACE ( NAME, '你', '你們?nèi)? ) )
三、postgre做空間數(shù)據(jù)分析
比如面相交
1、使用步驟 新建空間索引create extension postgis;創(chuàng)建geometry類型字段
3.插入geometry數(shù)據(jù)
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(chǎn)流區(qū)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(chǎn)流區(qū)單元');4.pg庫清空數(shù)據(jù)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(chuàng)建自增字段
設(shè)置為serial4類型
保存后自動加序列
2、重置自增序列號為指定數(shù)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(shù)值：TRUNCATE TABLE wl_model_polygon; TRUNCATE wl_model_polygon RESTART IDENTITY;
到此這篇關(guān)于postgresql使用dblink跨庫增刪改查的文章就介紹到這了,更多相關(guān)postgresql跨庫增刪改查內(nèi)容請搜索腳本之家以前的文章或繼續(xù)瀏覽下面的相關(guān)文章希望大家以后多多支持腳本之家！
相關(guān)文章
Postgresql數(shù)據(jù)庫之創(chuàng)建和修改序列的操作
這篇文章主要介紹了Postgresql數(shù)據(jù)庫之創(chuàng)建和修改序列的操作，具有很好的參考價值，希望對大家有所幫助。一起跟隨小編過來看看吧2021-02-02
PostgreSQL并行計算算法及參數(shù)強制并行度設(shè)置方法
這篇文章主要介紹了PostgreSQL 并行計算算法，參數(shù)，強制并行度設(shè)置,本文通過示例代碼給大家介紹的非常詳細(xì)，對大家的學(xué)習(xí)或工作具有一定的參考借鑒價值，需要的朋友可以參考下2022-04-04
PostgreSQL 實現(xiàn)distinct關(guān)鍵字給單獨的幾列去重
這篇文章主要介紹了PostgreSQL 實現(xiàn)distinct關(guān)鍵字給單獨的幾列去重，具有很好的參考價值，希望對大家有所幫助。一起跟隨小編過來看看吧2021-01-01
postgresql開啟pg_log日志詳細(xì)步驟及參數(shù)說明
pg_log日志要啟動保存的話需要去設(shè)置一下相關(guān)的配置文件參數(shù)就好了,下面這篇文章主要給大家介紹了關(guān)于postgresql開啟pg_log日志詳細(xì)步驟及參數(shù)說明的相關(guān)資料,需要的朋友可以參考下2024-02-02
PostgreSQL 提供了多種不同的復(fù)制表的方法，它們的差異在于是否需要復(fù)制表結(jié)構(gòu)或者數(shù)據(jù)，這篇文章主要介紹了PostgreSQL 復(fù)制表的 5 種方式,需要的朋友可以參考下2023-01-01
PostgreSQL中調(diào)用存儲過程并返回數(shù)據(jù)集實例
這篇文章主要介紹了PostgreSQL中調(diào)用存儲過程并返回數(shù)據(jù)集實例,本文給出一創(chuàng)建數(shù)據(jù)表、插入測試數(shù)據(jù)、創(chuàng)建存儲過程、調(diào)用創(chuàng)建存儲過程和運行效果完整例子,需要的朋友可以參考下2015-01-01
在開發(fā)過程中,經(jīng)常要取日期的年,月,日,小時等值,PostgreSQL 提供一個非常便利的EXTRACT函數(shù),這篇文章主要給大家介紹了關(guān)于PostgreSQL時間日期的語法及注意事項的相關(guān)資料,需要的朋友可以參考下2023-01-01
這篇文章主要介紹了postgreSQL中的case用法說明，具有很好的參考價值，希望對大家有所幫助。一起跟隨小編過來看看吧2021-01-01
postgresql 中的加密擴展插件pgcrypto用法說明
這篇文章主要介紹了postgresql 中的加密擴展插件pgcrypto用法說明，具有很好的參考價值，希望對大家有所幫助。一起跟隨小編過來看看吧2021-01-01
PostgreSQL索引掃描時為什么index only scan不返回ctid
這篇文章主要介紹了PostgreSQL索引掃描時為什么index only scan不返回ctid的原因探索，文中通過示例代碼介紹的非常詳細(xì)，對大家的學(xué)習(xí)或者工作具有一定的參考學(xué)習(xí)價值，需要的朋友們下面隨著小編來一起學(xué)習(xí)學(xué)習(xí)吧2022-09-09