阿里云轻量应用服务器Linux-Centos7下Oracle19c的配置

PLSQL Developer14连接Oracle详细过程 下载地址:Instant Client for Microsoft Windows (x64) 64-bit | Oracle 中国解压客户端工具到一个目录(解压文件到当前目录,自动合并成一个目录)​ 电脑(右键)→属性→高级系统设置→环境变量→系统环境变量→新建变量名:NLS_LANG 变量值: AMERICAN_AMERICA.ZHS16GBK​ 变量名:TNS_ADMIN 变量值:C:\xiaxinyu\software\Oracle\instantclient_19_10​ 在Oracle客户端(C 阅读详情

初始环境:阿里云轻量应用服务器已经安装Oracle19c
具体目标:配置Oracle Database 19c

第一步:切换到Oracle命令行

1.修改root用户密码,千万记得这一步,不然你会后悔的。
执行命令 passwd ,输入两次123456,就可以了。
在这里插入图片描述
2.切换到oracle用户下,执行命令su - oracle:
在这里插入图片描述
3.查看监听状态,执行命令 lsnrctl status:
在这里插入图片描述
监听启动,执行命令 lsnrctl start:
在这里插入图片描述
监听关闭,执行命令 lsnrctl stop;

4.登录oracle数据库,执行命令sqlplus / as sysdba。
在这里插入图片描述

第二步:新建用户和表空间

1.启动实例,执行命令 startup:
在这里插入图片描述

关闭实例,执行命令 shutdown;

tips:要是startup后报错ORA-01012: not logged on;

sqlplus / as sysdba
shutdonwn abort

来源:https://www.cnblogs.com/kerrycode/p/4244493.html

2.创建临时表空间:

--查询临时表空间文件的绝对路径。如果需要的话,可以通过查询来写定绝对路径。一般用${ORACLE_HOME}就可以了  
select name from v$tempfile;  
create temporary tablespace TESTLEAF_TEMP tempfile '${ORACLE_HOME}\oradata\TESTLEAF_TEMP.bdf' size 100m reuse autoextend on next 20m maxsize unlimited; 

在这里插入图片描述
3.创建正式表空间:

--查询用户表空间文件的绝对路径:
select name from v$datafile;
create tablespace TESTLEAF datafile '${ORACLE_HOME}\oradata\TESTLEAF.dbf' size 100M reuse autoextend on next 40M maxsize unlimited default storage(initial 128k next 128k minextents 2 maxextents unlimited);

在这里插入图片描述
4.创建用户和密码,指定上边创建的临时表空间和表空间(创建用户的时候用户名以C##开头即可,防止提示用户名无效):

create user C##TESTLEAF identified by 123456 default tablespace TESTLEAF temporary tablespace TESTLEAF_TEMP;

在这里插入图片描述
tips:
相比于oracle11g,oracle19c多了一个CDB和PDB的概念(从12C开始出现)
https://www.it610.com/article/1280868824853266432.htm

5.修改用户名名称
查询需要修改的用户名的编号:

select user#,name from user$;

在这里插入图片描述
(取对应的user#,这里user#等于113)
修改用户名称:

update user$ set name='TESTLEAF' where user#=113;

在这里插入图片描述
6.解决Oracle修改用户名后,再次修改该用户密码,会报‘找不到该用户’的问题:

ALTER SYSTEM FLUSH SHARED_POOL;

在这里插入图片描述
7.修改该用户对应的密码:

ALTER USER TESTLEAF IDENTIFIED BY 123456;

在这里插入图片描述
8.用户赋权:

grant dba to TESTLEAF;
grant connect,resource to TESTLEAF;
grant select any table to TESTLEAF;
grant delete any table to TESTLEAF;
grant update any table to TESTLEAF;
grant insert any table to TESTLEAF;
#操作表空间的权限
grant unlimited tablespace to TESTLEAF;

在这里插入图片描述
在这里插入图片描述
9.查看用户所有权限:

select * from dba_sys_privs where grantee = 'TESTLEAF';
select * from dba_role_privs where grantee = 'TESTLEAF';

第三步:切换用户

1.切换到刚刚创建的用户(用户名+密码):

conn TESTLEAF/123456;

2.检查当前用户:

show user;

在这里插入图片描述

第四步:在当前用户下创建一些表

1.创建表Customers

CREATE TABLE Customers
(
  cust_id      char(10)  NOT NULL ,
  cust_name    char(50)  NOT NULL ,
  cust_address char(50)  NULL ,
  cust_city    char(50)  NULL ,
  cust_state   char(5)   NULL ,
  cust_zip     char(10)  NULL ,
  cust_country char(50)  NULL ,
  cust_contact char(50)  NULL ,
  cust_email   char(255) NULL 
);

2.创建表OrderItems

CREATE TABLE OrderItems
(
  order_num  int          NOT NULL ,
  order_item int          NOT NULL ,
  prod_id    char(10)     NOT NULL ,
  quantity   int          NOT NULL ,
  item_price decimal(8,2) NOT NULL 
);

3.创建表Orders

CREATE TABLE Orders
(
  order_num  int      NOT NULL ,
  order_date date     NOT NULL ,
  cust_id    char(10) NOT NULL 
);

4.创建表Products

CREATE TABLE Products
(
  prod_id    char(10)      NOT NULL ,
  vend_id    char(10)      NOT NULL ,
  prod_name  char(255)     NOT NULL ,
  prod_price decimal(8,2)  NOT NULL ,
  prod_desc  varchar(1000) NULL 
);

5.创建表Vendors

CREATE TABLE Vendors
(
  vend_id      char(10) NOT NULL ,
  vend_name    char(50) NOT NULL ,
  vend_address char(50) NULL ,
  vend_city    char(50) NULL ,
  vend_state   char(5)  NULL ,
  vend_zip     char(10) NULL ,
  vend_country char(50) NULL 
);

6.定义主键

ALTER TABLE Customers ADD CONSTRAINT PK_Customers PRIMARY KEY (cust_id);
ALTER TABLE OrderItems ADD CONSTRAINT PK_OrderItems PRIMARY KEY (order_num, order_item);
ALTER TABLE Orders ADD CONSTRAINT PK_Orders PRIMARY KEY (order_num);
ALTER TABLE Products ADD CONSTRAINT PK_Products PRIMARY KEY (prod_id);
ALTER TABLE Vendors ADD CONSTRAINT PK_Vendors PRIMARY KEY (vend_id);

7.定义外键

ALTER TABLE OrderItems
ADD CONSTRAINT FK_OrderItems_Orders FOREIGN KEY (order_num) REFERENCES Orders (order_num);
ALTER TABLE OrderItems
ADD CONSTRAINT FK_OrderItems_Products FOREIGN KEY (prod_id) REFERENCES Products (prod_id);
ALTER TABLE Orders
ADD CONSTRAINT FK_Orders_Customers FOREIGN KEY (cust_id) REFERENCES Customers (cust_id);
ALTER TABLE Products
ADD CONSTRAINT FK_Products_Vendors FOREIGN KEY (vend_id) REFERENCES Vendors (vend_id);

第五步:给刚才的表添加一些数据

1.给表Customers添加数据

INSERT INTO Customers(cust_id, cust_name, cust_address, cust_city, cust_state, cust_zip, cust_country, cust_contact, cust_email)
VALUES('1000000001', 'Village Toys', '200 Maple Lane', 'Detroit', 'MI', '44444', 'USA', 'John Smith', 'sales@villagetoys.com');
INSERT INTO Customers(cust_id, cust_name, cust_address, cust_city, cust_state, cust_zip, cust_country, cust_contact)
VALUES('1000000002', 'Kids Place', '333 South Lake Drive', 'Columbus', 'OH', '43333', 'USA', 'Michelle Green');
INSERT INTO Customers(cust_id, cust_name, cust_address, cust_city, cust_state, cust_zip, cust_country, cust_contact, cust_email)
VALUES('1000000003', 'Fun4All', '1 Sunny Place', 'Muncie', 'IN', '42222', 'USA', 'Jim Jones', 'jjones@fun4all.com');
INSERT INTO Customers(cust_id, cust_name, cust_address, cust_city, cust_state, cust_zip, cust_country, cust_contact, cust_email)
VALUES('1000000004', 'Fun4All', '829 Riverside Drive', 'Phoenix', 'AZ', '88888', 'USA', 'Denise L. Stephens', 'dstephens@fun4all.com');
INSERT INTO Customers(cust_id, cust_name, cust_address, cust_city, cust_state, cust_zip, cust_country, cust_contact)
VALUES('1000000005', 'The Toy Store', '4545 53rd Street', 'Chicago', 'IL', '54545', 'USA', 'Kim Howard');

2.给表Vendors添加数据

INSERT INTO Vendors(vend_id, vend_name, vend_address, vend_city, vend_state, vend_zip, vend_country)
VALUES('BRS01','Bears R Us','123 Main Street','Bear Town','MI','44444', 'USA');
INSERT INTO Vendors(vend_id, vend_name, vend_address, vend_city, vend_state, vend_zip, vend_country)
VALUES('BRE02','Bear Emporium','500 Park Street','Anytown','OH','44333', 'USA');
INSERT INTO Vendors(vend_id, vend_name, vend_address, vend_city, vend_state, vend_zip, vend_country)
VALUES('DLL01','Doll House Inc.','555 High Street','Dollsville','CA','99999', 'USA');
INSERT INTO Vendors(vend_id, vend_name, vend_address, vend_city, vend_state, vend_zip, vend_country)
VALUES('FRB01','Furball Inc.','1000 5th Avenue','New York','NY','11111', 'USA');
INSERT INTO Vendors(vend_id, vend_name, vend_address, vend_city, vend_state, vend_zip, vend_country)
VALUES('FNG01','Fun and Games','42 Galaxy Road','London', NULL,'N16 6PS', 'England');
INSERT INTO Vendors(vend_id, vend_name, vend_address, vend_city, vend_state, vend_zip, vend_country)
VALUES('JTS01','Jouets et ours','1 Rue Amusement','Paris', NULL,'45678', 'France');

3.给表Products添加数据

INSERT INTO Products(prod_id, vend_id, prod_name, prod_price, prod_desc)
VALUES('BR01', 'BRS01', '8 inch teddy bear', 5.99, '8 inch teddy bear, comes with cap and jacket');
INSERT INTO Products(prod_id, vend_id, prod_name, prod_price, prod_desc)
VALUES('BR02', 'BRS01', '12 inch teddy bear', 8.99, '12 inch teddy bear, comes with cap and jacket');
INSERT INTO Products(prod_id, vend_id, prod_name, prod_price, prod_desc)
VALUES('BR03', 'BRS01', '18 inch teddy bear', 11.99, '18 inch teddy bear, comes with cap and jacket');
INSERT INTO Products(prod_id, vend_id, prod_name, prod_price, prod_desc)
VALUES('BNBG01', 'DLL01', 'Fish bean bag toy', 3.49, 'Fish bean bag toy, complete with bean bag worms with which to feed it');
INSERT INTO Products(prod_id, vend_id, prod_name, prod_price, prod_desc)
VALUES('BNBG02', 'DLL01', 'Bird bean bag toy', 3.49, 'Bird bean bag toy, eggs are not included');
INSERT INTO Products(prod_id, vend_id, prod_name, prod_price, prod_desc)
VALUES('BNBG03', 'DLL01', 'Rabbit bean bag toy', 3.49, 'Rabbit bean bag toy, comes with bean bag carrots');
INSERT INTO Products(prod_id, vend_id, prod_name, prod_price, prod_desc)
VALUES('RGAN01', 'DLL01', 'Raggedy Ann', 4.99, '18 inch Raggedy Ann doll');
INSERT INTO Products(prod_id, vend_id, prod_name, prod_price, prod_desc)
VALUES('RYL01', 'FNG01', 'King doll', 9.49, '12 inch king doll with royal garments and crown');
INSERT INTO Products(prod_id, vend_id, prod_name, prod_price, prod_desc)
VALUES('RYL02', 'FNG01', 'Queen doll', 9.49, '12 inch queen doll with royal garments and crown');

4.给表Orders添加数据

INSERT INTO Orders(order_num, order_date, cust_id)
VALUES(20005, TO_DATE('2012-05-01', 'yyyy-mm-dd'), '1000000001');
INSERT INTO Orders(order_num, order_date, cust_id)
VALUES(20006, TO_DATE('2012-01-12', 'yyyy-mm-dd'), '1000000003');
INSERT INTO Orders(order_num, order_date, cust_id)
VALUES(20007, TO_DATE('2012-01-30', 'yyyy-mm-dd'), '1000000004');
INSERT INTO Orders(order_num, order_date, cust_id)
VALUES(20008, TO_DATE('2012-02-03', 'yyyy-mm-dd'), '1000000005');
INSERT INTO Orders(order_num, order_date, cust_id)
VALUES(20009, TO_DATE('2012-02-08', 'yyyy-mm-dd'), '1000000001');

5.给表OrderItems添加数据

INSERT INTO OrderItems(order_num, order_item, prod_id, quantity, item_price)
VALUES(20005, 1, 'BR01', 100, 5.49);
INSERT INTO OrderItems(order_num, order_item, prod_id, quantity, item_price)
VALUES(20005, 2, 'BR03', 100, 10.99);
INSERT INTO OrderItems(order_num, order_item, prod_id, quantity, item_price)
VALUES(20006, 1, 'BR01', 20, 5.99);
INSERT INTO OrderItems(order_num, order_item, prod_id, quantity, item_price)
VALUES(20006, 2, 'BR02', 10, 8.99);
INSERT INTO OrderItems(order_num, order_item, prod_id, quantity, item_price)
VALUES(20006, 3, 'BR03', 10, 11.99);
INSERT INTO OrderItems(order_num, order_item, prod_id, quantity, item_price)
VALUES(20007, 1, 'BR03', 50, 11.49);
INSERT INTO OrderItems(order_num, order_item, prod_id, quantity, item_price)
VALUES(20007, 2, 'BNBG01', 100, 2.99);
INSERT INTO OrderItems(order_num, order_item, prod_id, quantity, item_price)
VALUES(20007, 3, 'BNBG02', 100, 2.99);
INSERT INTO OrderItems(order_num, order_item, prod_id, quantity, item_price)
VALUES(20007, 4, 'BNBG03', 100, 2.99);
INSERT INTO OrderItems(order_num, order_item, prod_id, quantity, item_price)
VALUES(20007, 5, 'RGAN01', 50, 4.49);
INSERT INTO OrderItems(order_num, order_item, prod_id, quantity, item_price)
VALUES(20008, 1, 'RGAN01', 5, 4.99);
INSERT INTO OrderItems(order_num, order_item, prod_id, quantity, item_price)
VALUES(20008, 2, 'BR03', 5, 11.99);
INSERT INTO OrderItems(order_num, order_item, prod_id, quantity, item_price)
VALUES(20008, 3, 'BNBG01', 10, 3.49);
INSERT INTO OrderItems(order_num, order_item, prod_id, quantity, item_price)
VALUES(20008, 4, 'BNBG02', 10, 3.49);
INSERT INTO OrderItems(order_num, order_item, prod_id, quantity, item_price)
VALUES(20008, 5, 'BNBG03', 10, 3.49);
INSERT INTO OrderItems(order_num, order_item, prod_id, quantity, item_price)
VALUES(20009, 1, 'BNBG01', 250, 2.49);
INSERT INTO OrderItems(order_num, order_item, prod_id, quantity, item_price)
VALUES(20009, 2, 'BNBG02', 250, 2.49);
INSERT INTO OrderItems(order_num, order_item, prod_id, quantity, item_price)
VALUES(20009, 3, 'BNBG03', 250, 2.49);

6.提交操作

commit;

第六步:下载最新版的instantclient

1.由于Oracle官网下载较慢,于是提供了下方的百度网盘链接。
链接: https://pan.baidu.com/s/1CWR5ka5z7WDZ91iM0UsUMg 提取码: leaf
感兴趣的小伙伴也可以按照下面的方法在Oracle官网进行下载。
2.打开网站:https://www.oracle.com/database/technologies/instant-client.html
3.点击“Download”:
在这里插入图片描述
4.点击“Instant Client for Microsoft Windows (x64)”:
在这里插入图片描述
5.依次点击下载:Basic Package、SQL*Plus Package、SDK Package;
Basic Package
SQL*Plus Package
SDK Package
6.下载后进行解压,放进同一个文件夹
在这里插入图片描述

第七步:Navicat配置

1.打开Navicat,点击“工具”》“选项”:
在这里插入图片描述
2.点击“环境”,选择好刚才下载的文件夹里面对应的sqlplus.exe文件和oci.dll文件,点击“确定”,并重启Navicat,这样在Navicat中就可以对Oracle数据库使用sqlplus命令行界面了。
在这里插入图片描述
3.阿里云防火墙添加1521端口
在这里插入图片描述
在这里插入图片描述
不然就会出现如下错误:ORA-12170
在这里插入图片描述
4.服务名查询
在数据库中输入查询语句,
select global_name from global_name;
在这里插入图片描述
我这里是ORCLCDB,把这个记住。

5.Navicat使用刚才创建的用户新建连接
在这里插入图片描述
6.找到与该用户名相同的表空间,可以看到刚才创建的五张表,里面的数据也别无二致;
在这里插入图片描述

免责声明:
1.编写此文是为了更好地学习Oracle的配置,如果损害了有关人的利益,请联系删除;
2.如果文中描述欠妥,请在评论中进行指正;
3.文字编写不易,若感觉有用,点赞收藏关注会让博主很开心哦;
4.此外,本文支持任何形式的转载,转载请注明出处,非常感谢!!!
本文源自:https://blog.youkuaiyun.com/testleaf/article/details/109096654
博客园cnblogs同号。

敲敲云部署方案深度解析:命令安装与Docker选型决策指南 零代码平台私有化部署是低代码落地的关键门槛,其本质是环境一致性、运维可控性与安全合规性的系统权衡。命令安装将Linux系统级细节(systemd服务、内核参数、包依赖)完全暴露,适合需审计二进制、对接国产数据库或运行于物理机/信创环境的场景;Docker安装则通过镜像封装实现跨发行版环境隔离,但引入存储膨胀、调试黑盒与网络穿透等新挑战。二者差异远超工具选择,实为责任边界的重新划分——前者交付控制权,后者交付可复现性。本文聚焦敲敲云这一典型APaaS平台,结合4核8G云服务器实测数据、17个高频血泪坑及五类企 阅读详情

相关推荐

Bower在VPS上的遗产系统维保指南(2024实操版)

Bower是一种已归档的前端依赖管理工具,其核心原理是基于Git协议克隆仓库并平铺式存放静态资源,依赖Node.js和npm运行。尽管已被npm、yarn、pnpm等现代包管理器全面取代,但在遗留系统维护场景中仍具不可替代性——尤其当项目深度耦合Bootstrap 3、jQuery 1.x或运行于离线/嵌入式VPS环境时。本文聚焦真实VPS部署(Ubuntu 24.04/CentOS Stream 9),解析Bower与npm的共生关系、Git网络适配机制及权限模型冲突本质,提供用户级安装、SSL/TLS加

afeyfre41671的博客 414

Windows 安装 Oracle 19c Instant Client

本文介绍了Oracle Instant Client的安装配置流程:首先从官网下载安装包并解压至指定目录;然后配置系统环境变量ORACLE_HOME和Path;通过CMD运行sqlplus/nolog验证安装是否成功。文章特别强调必须将basic和sqlplus解压到同一目录,最后说明了如何使用Navicat Premium通过OCI环境连接Oracle 19数据库。整个安装过程需要注意环境变量配置和文件解压路径的正确性。

星空椰 1796

Oracle Instant Client for Microsoft Windows (x64) 64-bit

All files required to run OCI, OCCI, and JDBC:OCI applications: instantclient-basic-win64-10.2.0.5.zip instantclient-basic-windows.x64-11.2.0.4.0.zip instantclient-basic-windows.x64-12.1.0.2.0.zip instantclient-basic-windows.x64-12.2.0.1.0.zip instantclient-basic-windows.x64-18.5.0.0.0dbru.zip

阿里云轻量应用服务器Linux-Centos7下Oracle19c的安装

服务器Linux-Centos7下Oracle19c的安装 ORA-12170 ORA-12514 ORA-12505 检查Linux系统版本 oracle-database-ee-19c-1.0-1.x86_64.rpm Oracle退格键和上下键 Navicat连接Oracle数据库

testleaf.cn 7399

instantclient 19

可用于windows Navicat 15 连接 oracle19

工业预测模型避坑指南:PHM2012轴承数据实战中,LSTM、CNN、Transformer到底怎么选?

本文深入探讨了工业预测模型在PHM2012轴承数据下的选型策略,对比了LSTM、CNN和Transformer等主流模型在轴承寿命预测任务中的表现。通过真实实验数据,提供了可复现的选型决策框架,帮助工程师根据数据规模、硬件条件和精度要求选择最佳模型。文章还揭示了工程落地中的隐藏陷阱,如数据不均衡和工况迁移挑战,并提出了解决方案。

weixin_33694172的博客 493

python3连接oracle 11G数据库

Python连接Oracle数据库 环境说明(保持各个关联环境版本对应非常重要): 系统环境:win10 64位 数据库版本:Oracle 11G 64位 python版本:python 3.7.8 64位 操作步骤 1、安装databaselibrary 库 pip安装,具体安装略 2、安装oracle 客户端 下载oracle客户端需和oracle版本对应 我根据本地环境下载的是instantclient-basic-windows.x64-11.2.0.4.0.zip ..

lixiaomei0623的专栏 2599

LabVIEW与VisionMaster融合开发:从封装避坑到高效集成的实战指南

本文为LabVIEW开发者提供了一套与海康VisionMaster融合开发的实战指南。针对直接调用SDK时遇到的程序集加载失败和面向对象编程冲突等核心障碍,文章详细介绍了通过C#进行“浅封装”的解决方案,包括封装显示控件和核心功能类,并给出了在LabVIEW中高效集成、调用及资源管理的最佳实践,旨在帮助开发者稳定、高效地集成机器视觉功能。

weixin_29210727的博客 153

【Oracle学习笔记_01】Win11虚拟机安装Oracle 19c(保姆教程)

Oracle19c的安装配置 instant Client 虚拟机 服务器

m0_47109389的博客 1万+

给STM32供电,别只接VDD和GND!VBAT、VDDA这些引脚不接会怎样?

本文深入解析STM32电源设计中VBAT与VDDA引脚的关键作用,揭示仅连接VDD和GND可能导致的ADC精度下降、RTC功能异常等问题。通过实测数据对比不同连接方案,提供包括滤波电路设计、PCB布局布线和双电源自动切换在内的实用解决方案,帮助开发者优化系统稳定性与性能。

weixin_30521161的博客 621

HID设备开发避坑指南:如何利用Windows内置驱动快速实现USB键盘模拟

本文详细解析了HID设备开发中的免驱方案与USB键盘模拟技术,重点介绍了HID协议的核心优势、开发基础架构及Windows平台的实现方法。通过实战案例和代码示例,帮助开发者快速掌握USB键盘模拟技术,避免常见开发陷阱,提升设备兼容性和性能。

lambda的博客 682

【Oracle 19c 及 21c】Windows 平台客户端包差异

Oracle 19c 以后客户端安装包的差异。

影三人 2583

PCB Layout总结

PCB Layout作为硬件设计中的一个环节,也是很重要的一个环节;在硬件电路设计合理的情况下,他其实是影响性能的一个绝对重要的指标。现在很多的PCB Layout工程师都是按照硬件工程师或者PI SI工程师给出的约束规则来完成布局布线的,这些也就是俗称的 “拉线工”。他们重复而机械的完成一块块PCB设计,一段时间设计后,他们中的一些或许已经有了这样的一些经验:哪些要做等长,哪些要走粗、哪些要平...

xydlxd118的博客 2582

Oracle19c详细安装、配置、卸载教程

Oracle19c详细安装、配置、卸载教程(包含PL/SQL安装连接Oracle配置、Navicat连接Oracle配置)

qq_33267595的博客 1万+

Spring WebFlux响应式编程实战(三):优雅处理异常与高效调试技巧

本文深入探讨了Spring WebFlux响应式编程中的异常处理与调试技巧。文章通过电商订单流处理场景,详细解析了Reactor核心操作符onErrorReturn、onErrorResume和onErrorMap的实战应用,并介绍了如何构建@ControllerAdvice和WebExceptionHandler全局异常处理防线。同时,提供了使用.log()操作符、Hooks.onOperatorDebug()及BlockHound进行高效调试的方法,旨在帮助开发者构建健壮、可维护的响应式应用。

weixin_29252171的博客 65

利用腾讯云轻量服务器快速搭建网站CDN

腾讯云的轻量最近在主机和站长中间可谓是占尽来风头,优秀的线路,低廉的价格使得越来越多的人开始使用腾讯云的轻量级服务器来部署应用。由于腾讯云轻量服务器的海外服务器电信走的是CN2GIA线路带宽30m挺适合做CDN的,我就记录一下如何利用腾讯云香港的轻量服务器搭建一个CDN。 购买服务器 首先选购服务器,最近腾讯云在做活动,力度很大国内备案用户可以选用国内的服务器最低128元/年,未备案的可以选用香港或者新加坡的服务器。 购买链接 服务器系统选Debian、Ubuntu、centos都可以 一键脚

demo_top的博客 2564

【Oracle客户端部署】保姆级Oracle 19c Client客户端安装与配置:下载安装+tnsnames.ora配置+环境变量配置+PLSQL软件配置+PLSQL连接

一篇就够:Oracle 19c Client(19.3) 安装到 PL/SQL Developer 连接的全流程

「YYL DBA手记」 数据库 · 架构 · 运维 · 实战 yyldba.blog.youkuaiyun.com 4610

BAT031:按列表名单将路径a下的文件夹批量剪切到路径b

摘要:按列表名单将路径a下的文件夹批量剪切到路径b。

qq_31762031的博客 1218

云服务器部署Oracle(主要解决部署过程中遇到的问题)

​ Oracle 12C以及更高版本中引入了CDB与PDB的新特性,在ORACLE 12C数据库引入的多租用户环境(Multitenant Environment)中,允许一个数据库容器(CDB)承载多个可插拔数据库(PDB)。CDB全称为Container Database,中文翻译为数据库容器,PDB全称为Pluggable Database,即可插拔数据库。

qq_63189148的博客 1208

ssm框架搭建

这个是一个在idea上成功跑起来的ssm框架项目,保存在这里方便以后使用直接下载

深度学习工具包 DeepLearningToolbox

深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/) or [tensorflow](http://www.tensorflow.org/)I would suggest you use one of the tools mentioned above rather than use this toolbox.Best, Rasmus.DeepLearnToolbox================A Matlab toolbox for Deep Learning.Deep Learning is a new subfield of machine learning that focuses on learning deep hierarchical models of data.It is inspired by the human brain's apparent deep (layered, hierarchical) architecture.A good overview of the theory of Deep Learning theory is[Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf)For a more informal introduction, see the following videos by Geoffrey Hinton and Andrew Ng.* [The Next Generation of Neural Networks](http://www.youtube.com/watch?v=AyzOUbkUf3M) (Hinton, 2007)* [Recent Developments in Deep Learning](http://www.youtube.com/watch?v=VdIURAu1-aU) (Hinton, 2010)* [Unsupervised Feature Learning and Deep Learning](http://www.youtube.com/watch?v=ZmNOAtZIgIk) (Ng, 2011)If you use this toolbox in your research please cite [Prediction as a candidate for learning deep hierarchical models of data](http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6284)```@MASTERSTHESIS\{IMM2012-06284, author = "R. B. Palm", title = "Prediction as a candidate for learning deep hierarchical models of data", year = "2012",}```Contact: rasmusbergpalm at gmail dot comDirectories included in the toolbox-----------------------------------`NN/` - A library for Feedforward Backpropagation Neural Networks`CNN/` - A library for Convolutional Neural Networks`DBN/` - A library for Deep Belief Networks`SAE/` - A library for Stacked Auto-Encoders`CAE/` - A library for Convolutional Auto-Encoders`util/` - Utility functions used by the libraries`data/` - Data used by the examples`tests/` - unit tests to verify toolbox is workingFor references on each library check REFS.mdSetup-----1. Download.2. addpath(genpath('DeepLearnToolbox'));Example: Deep Belief Network---------------------```matlabfunction test_example_DBNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit RBM and visualize its weightsrand('state',0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand('state',0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn, 10);nn.activation_function = 'sigm';%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.10, 'Too big error');```Example: Stacked Auto-Encoders---------------------```matlabfunction test_example_SAEload mnist_uint8;train_x = double(train_x)/255;test_x = double(test_x)/255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN% Setup and train a stacked denoising autoencoder (SDAE)rand('state',0)sae = saesetup([784 100]);sae.ae{1}.activation_function = 'sigm';sae.ae{1}.learningRate = 1;sae.ae{1}.inputZeroMaskedFraction = 0.5;opts.numepochs = 1;opts.batchsize = 100;sae = saetrain(sae, train_x, opts);visualize(sae.ae{1}.W{1}(:,2:end)')% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = 'sigm';nn.learningRate = 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.16, 'Too big error');```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x',28,28,60000))/255;test_x = double(reshape(test_x',28,28,10000))/255;train_y = double(train_y');test_y = double(test_y');%% ex1 Train a 6c-2s-12c-2s Convolutional neural network %will run 1 epoch in about 200 second and get around 11% error. %With 100 epochs you'll get around 1.2% errorrand('state',0)cnn.layers = { struct('type', 'i') %input layer struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %sub sampling layer struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %subsampling layer};cnn = cnnsetup(cnn, train_x, train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn, train_x, train_y, opts);[er, bad] = cnntest(cnn, test_x, test_y);%plot mean squared errorfigure; plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex4 neural net with sigmoid activation functionrand('state',0)nn = nnsetup([784 100 10]);nn.activation_function = 'sigm'; % Sigmoid activation functionnn.learningRate = 1; % Sigm require a lower learning rateopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex5 plotting functionalityrand('state',0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = 'softmax'; % use softmax outputopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex6 neural net with sigmoid activation and plotting of validation and training error% split training data into training and validation datavx = train_x(1:10000,:);tx = train_x(10001:end,:);vy = train_y(1:10000,:);ty = train_y(10001:end,:);rand('state',0)nn = nnsetup([784 20 10]); nn.output = 'softmax'; % use softmax outputopts.numepochs = 5; % Number of full sweeps through dataopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally)[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');```[![Bitdeli Badge](https://d2weczhvl823v0.cloudfront.net/rasmusbergpalm/deeplearntoolbox/trend.png)](https://bitdeli.com/free "Bitdeli Badge")

【Oracle19C】Windows安装oracle instance client

【Oracle19C】Windows安装oracle instance client

qq_41466440的博客 3670

迈瑞BS系列全自动生化分析仪LIS协议接口手册(内含HL7和ASTM)

迈瑞BS系列全自动生化分析仪LIS协议接口手册,内含HL7和ASTM)

Oracle19c客户端安装与配置

Oracle客户端是一种用于连接和与Oracle数据库进行通信的软件工具。Oracle客户端可以在各种操作系统上运行,包括Windows、Linux、Unix等。Oracle客户端提供了多种编程接口,如ODBC(开放数据库连接)、JDBC(Java数据库连接)和OCI(Oracle Call Interface),使得开发者可以使用不同的编程语言(如C++、Java、Python等)与Oracle数据库进行交互。

weixin_68999643的博客 6301

VB摄像头监控

该VB模块可对摄像头捕获的画面进行录像等,可在创建的窗口进行控制

CentOS 7.9 阿里云服务器安装 Oracle 数据库(基于 Docker 快速部署与远程连接)

yum-config-manager --add-repo http://mirrors.aliyun.com/docker-ce/linux/centos/docker-ce.repo(添加软件源)docker pull registry.cn-hangzhou.aliyuncs.com/helowin/oracle_11g(拉取镜像,因为Oracle差不多3G,所以这一步要等一会,), docker images(查看docker容器里面有没有我们安装的Oracle)//查看服务器的CPU个数。

大马猴5的博客 652

【亲测免费】 OracleClient-19C:全平台兼容的Oracle数据库客户端推荐

OracleClient-19C:全平台兼容的Oracle数据库客户端推荐 【下载地址】OracleClient-19C全平台兼容的Oracle数据库客户端 OracleClient-19C: 全平台兼容的Oracle数据库客户端欢迎使用OracleClient-19C资源包,本资源提供了全面的Oracle客户端软件,支...

gitblog_09806的博客 852
上一篇: 阿里云轻量应用服务器Linux-Centos7下MySQL8.0.19的使用
下一篇: 阿里云轻量应用服务器Linux-Centos7下Oracle19c的安装
testleaf
博客等级 码龄7年 3514粉丝 72原创
评论 6
成就一亿技术人!
拼手气红包6.0元
还能输入1000个字符
 
 条评论被折叠 查看
添加红包

请填写红包祝福语或标题

个

红包个数最小为10个

元

红包金额最低5元

当前余额3.43元 前往充值 >
需支付:10.00元
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包

打赏作者

testleaf

你的鼓励将是我创作的最大动力

¥1 ¥2 ¥4 ¥6 ¥10 ¥20
扫码支付:¥1
获取中
扫码支付

您的余额不足,请更换扫码支付或充值

打赏作者

实付元
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值