Mysql连接失败的问题

MYSQL出现 Client does not support authentication 的解决方法 MYSQL 帮助: A.2.3 Client does not support authentication protocol MySQL 4.1 and up uses an authentication protocol based on a password hashing algorithm that is incompatible with that used by older clients. If you upgrade the server to 4.1, attempts to connect to it with an older client may fail with 立即下载

安装什么的问题就不说了,就记录一下连接数据库失败的一些解决方法。

一..报错。com.mysql.jdbc.exceptions.jdbc4.MySQLNonTransientConnectionException: Client does not support authentication protocol requested by server; consider upgrading MySQL client

public static void main(String[] args) throws ClassNotFoundException {
		String driver ="com.mysql.jdbc.Driver";
		String url="jdbc:mysql://localhost:3306/myblog;
		String username="localhost";
		String password="123456";
		Class.forName(driver);
		getConn(url, username, password);
		
	}
	public static Connection getConn(String url, String username,String password){
		Connection conn = null;
		try {
			conn = DriverManager.getConnection(url, username, password);
			System.out.println("数据库连接成功!!");
		} catch (SQLException e) {
			e.printStackTrace();
			throw new RuntimeException("数据库连接失败!!");
		}
		return conn;
	}

这种报错可能就是用户和密码的问题,如果不知道的可以创建一个新的用户进行连接。创建方法如下:

1.打开命令行cmd

2.开启数据库:net start Mysql

3.登陆数据库

4.更改数据库的加密方式输入:ALTER USER 'root'@'localhost' IDENTIFIED BY 'password' PASSWORD EXPIRE NEVER;

5.更改密码: ALTER USER 'root'@'localhost' IDENTIFIED WITH mysql_native_password BY '123';

6.刷新权限:FLUSH PRIVILEGES;

7.在客户端就可连接成功。

二.可能eclipse会继续报错

Unknown initial character set index '255' received from server. Initial client character set can be forced via the 'characterEncoding' property.

这个就是url的路径出问题了,编码格式不对需要在后面添加?useUnicode=true&characterEncoding=utf8";

        String url="jdbc:mysql://localhost:3306/myblog?useUnicode=true&characterEncoding=utf8";

这样之后就没问题了。

 

若依(RuoYi)框架从零到部署:Spring Boot+Vue权限系统完整配置指南 在Java企业级应用开发中,Spring Boot作为构建微服务和单体应用的基石,极大地简化了项目初始化和配置流程。其核心原理在于通过自动配置和起步依赖,快速整合数据访问、安全认证、缓存等常用模块,显著提升开发效率。这一技术价值在需要快速构建后台管理系统的场景中尤为突出,例如权限管理、用户角色分配等通用需求。若依(RuoYi)框架正是基于Spring Boot和Vue.js的优秀实践,它封装了RBAC权限模型、代码生成器等高频功能,提供了开箱即用的解决方案。本文聚焦于若依框架的【环境配置】与【项目部署】,通 阅读详情

相关推荐

软件测试--用例篇(测试用例的好处、测试用例的七种设计方法、测试用例的粒度、测试用例的评价)

作为软件测试工程师,最主要的工作就是:编写测试用例,那么为什么要编写测试用例?它有什么好处?如何编写测试用例?测试用例写简单好还是复杂好?如何评价测试用例的质量? 天呐撸!能说不会吗?! 作为倾向软件测试工程师的老铁们,还是一起来学习吧! 一、编写测试用例的好处 测试用例是测试者的依据 测试用例使得工作可重复,是自动化测试的基础 补充:自动化测试的前提条件是:功能比较稳定,页面...

cherrydreamsover的博客 3734

JDBC 获取数据库连接时报异常 MySQLNonTransientConnectionException

问题描述 在手写 获取数据库连接 时,发生如下的报错: com.mysql.jdbc.exceptions.jdbc4.MySQLNonTransientConnectionException: Client does not support authentication protocol requested by server; consider upgrading MySQL client 代码如下: public class ConnectionTest { @Test publi

PandaMan~的博客 3686

混合示教长短时记忆网络的车辆轨迹预测研究

论文"混合示教长短时记忆网络的车辆轨迹预测研究"中数据处理及MTF-LSTM模型实现代码,模型在Pytorch框架下实现。

ColinFhz的博客 1444

异常:com.mysql.jdbc.exceptions.MySQLNonTransientConnectionException:Client does not support authentica

异常:com.mysql.jdbc.exceptions.MySQLNonTransientConnectionException:Client does not support authentication protocol requested by server;consider upgrading MySQL client 原因:mysql8以前的版本加密规则是mysql_native_password,而mysql8之后,加密规则是caching_sha2_password。如果你使用mysql8.

qq_41530024的博客 2056

番茄(西红柿)叶片病害检测数据集:12k+图像,10类,yolo标注

本文介绍了包含12,250张图像的番茄叶片病害检测数据集,涵盖10种常见病害类型及健康叶片,采用YOLO格式标注。数据集具有三大核心价值:科研层面填补病害样本空白,推动算法创新;产业应用支持精准诊断、早期预警,降低损失20-40%;社会生态方面减少农药使用15-30%,助力绿色农业。该数据集作为AI与农业结合的数字基础设施,适用于计算机视觉项目、科研实验等场景。

m0_73878864的博客 1131

uniapp(微信小程序) - 实现uniapp小程序电子签名板,横屏手写姓名签名专用写字画板,客户专用签字板,手写签名签字画板组件,手写签名,(详细运行示例,一键复制开箱即用)

uniapp实现微信小程序的电子签名效果,uniapp微信小程序 电子签名 写字板,用uniapp实现微信小程序的电子签名,uniapp微信小程序canvans实现手写签名,记录一个uniapp写的小程序的手写板,横屏,用于签名,unniapp实现电子签名,uniapp怎么做横屏的手写签名,uniapp如何实现签字板,uniapp小程序平台下怎么做电子签名板组件,uniapp电子签名插件,uniapp最好用的电子写字板插件组件源码,实现uniapp 手写签名功能,手写板、签字板,uni-app插件市场最好的

✅ 精选推荐文章 1万+

...jdbc4.MySQLNonTransientConnectionException: Client does not support authentication protocol

错误信息如下: 十月 31, 2019 3:34:30 下午 org.springframework.context.support.AbstractApplicationContext prepareRefresh 信息: Refreshing org.springframework.context.support.ClassPathXmlApplicationContext@685f4c2e...

6283

【项目实战】AES实施应符合NIST密码算法验证程序

AES由美国国家标准与技术研究院(NIST)制定。NIST是负责开发和维护美国政府使用的技术标准的机构,包括加密标准。

奶爸的编程之路,也就一周冷个三天 765

Navicate连接mysql 报错

原因 mysql8 之前的版本中加密规则是mysql_native_password,而在mysql8之后,加密规则是caching_sha2_password 解决 ALTER USER 'admin'@'%' IDENTIFIED BY 'admin' PASSWORD EXPIRE NEVER; #更改加密方式 ALTER USER 'admin'@'%' IDENTIFIED WITH mysql_native_password BY 'admin'; #更新用户密码 FLUSH PRIVIL

木头人的博客 258

YOLO26模型应用过程中的报错处理及疑问解答,涉及环境搭建、模型训练、模块改进、论文写作

本文记录了YOLO26模型的这一系列过程中可能发生的问题,以及大家非常关心的问题,这里统一做了一个记录和解答。

Limiiiing的博客 239

用 XCO 库一键找出某个 ABAP 类的全部子类

ABAP XCO库子类枚举技术解析 本文介绍了在ABAP项目中如何利用XCO库高效获取类的继承层次结构。传统方法使用CL_ABAP_CLASSDESCR或手动维护映射表存在效率低、易遗漏的问题。新方法通过XCO库的IF_XCO_AO_CLASS接口,可简洁地获取父类的所有子类(包括间接子类)。 核心API链式调用: 获取类句柄:xco_cp_abap=>class() 导航子类集合:subclasses->all 获取结果:get()返回类引用表或get_names()返回类名列表 典型应用场景

2007 年 ~ 2025 年,深耕 SAP 技术 18 年 127

mysql与php连接失败的原因_如何解决php mysql连接失败的问题

如何解决php mysql连接失败的问题发布时间:2020-07-28 09:45:42来源:亿速云阅读:91作者:Leah今天就跟大家聊聊有关如何解决php mysql连接失败的问题,可能很多人都不太了解,为了让大家更加了解,小编给大家总结了以下内容,希望大家根据这篇文章可以有所收获。php mysql连接失败是因为php高版本已经不再使用“mysql_connect()”来连接MySQL数据库...

weixin_32504329的博客 1148

当AI学会写“自传”:OpenClaw 的 SOUL.md 如何把配置文件变成一颗会变形的心

SOUL.md。

步子哥的博客 1309

Springboot中mysql连接失败问题

解决方案:这是时区问题,spring.datasource.dynamic.datasource.mysql.url=jdbc:mysql://localhost:3306/user_profile_manager1214?解决方案:1.有可能是mysql使用8.0版本,但驱动仍使用老版本,将application中:spring.datasource.dynamic.datasource.mysql.driver-class-name=com.mysql.cj.jdbc.Driver。

qq_33686920的博客 1197

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")

SIMATIC S7-1200, S7-1500 PID 控制[手册]

SIMATIC S7-1200, S7-1500 PID 控制[手册]

IDEA连接MySQL及其驱动程序jar包下载失败问题

解决IDEA连接MySQL的问题,包括mysql的下载与安装、mysql驱动文件的下载,以及解决idea连接mysql失败的问题

2302_80713883的博客 2607

Data-Structures-and-Algorithm-Analysis-in-Java.pdf

作者: Weiss, Mark A. 出版社: Addison Wesley Data Structures and Algorithm Analysis in Java is an “advanced algorithms” book that fits between traditional CS2 and Algorithms Analysis courses. In the old ACM Curriculum Guidelines, this course was known as CS7. This text is for readers who want to learn good programming and algorithm analysis skills simultaneously so that they can develop such programs with the maximum amo...

Mysql偶尔连接失败的问题

在项目中,偶尔会出现获取jdbc连接失败的报错 其中有一种因为连接失效的报错: The last packet successfully received from the server was 1,312,227 milliseconds ago. The last packet sent successfully to the server was 1,312,266 milliseconds ago. 说明我们的程序首次获取数据的Connection资源,从而进行正常的DB读写操作。但是在下次进行

qq_31856061的博客 2783

FineReport定义数据连接mysql失败问题

FineReport定义数据连接mysql失败问题 问题描述 做报表相关的东西时,连接mysql数据库,总是连接失败,水平低,URL上一顿乱改乱试,都不行,只能找度娘了. 问题原因 这是下载FineReport之后,webapps中的libs自带的jar包 这是自己下载以后替换的jar包 jar包版本过低,哈哈,有同样问题的小伙伴,希望能帮到你 ...

weixin_44627459的博客 3060

如何排查和解决PHP连接数据库MYSQL失败写锁的问题

通过本文的介绍,您可以系统地了解如何排查和解决PHP连接MySQL数据库失败及写锁问题。通过检查配置、确保服务启动、调整防火墙设置和用户权限,以及识别和解决长时间运行的事务和死锁问题,可以有效地保障应用的稳定运行。

cici15874的博客 1508

C++连接Mysql问题--SQLConnect()函数连接失败

问题描述: 使用C++连接mysql。用的时ODBC的方式进行连接。前面的分配环境句柄、设置环境属性和分配连接句柄都没有问题,但是进行SQLConnect(),数据源连接的时候,就会出现连接失败的问题。 解决办法在文末,过程可不看。 问题代码: SQLConnect(myConnetHandle, (SQLWCHAR*)"dwq", SQL_NTS, (SQLWCHAR*)"root", SQL_NTS, (SQLWCHAR*)"666666", SQL_NTS); 排除了数据源名字、用户名和密

L946494的博客 2048

Mysql远程连接失败问题

mysql远程在修改过用户权限防火墙关闭和端口号正常打开后连接依旧失败

weixin_45320235的博客 1494

mysql远程连接失败问题解决

之前一直是在本机登录mysql服务器,偶然试了一下使用mysql远程连接阿里云服务器,连接失败了!解决了以下三个问题以后成功实现连接。原因总结下来无非是以下两个:①阿里云防火墙端口未开放 ②mysql默认设置仅允许本机登录 1. 修改阿里云防火墙端口,增加对mysql服务器端口3306的放行: 2. 重新连接报错:ERROR 2003 (HY000): Can't connect to M...

携手天下 518

DBeaver连接mysql驱动下载失败问题

DBeaver连接mysql驱动下载失败问题 将原地址修改成图中地址。再次进行下载。 完成后有可能出现如下问题 使用doc登录mysql 例如:mysql -h要连接的ip -uroot -p密码 -P端口 mysql -uroot -p123456 查看使用为systom时间 修改为中国时间 再次使用DBeaver连接mysql数据库 成功! ...

Jesusloveus的博客 1万+

mysql 客户端无法连接_完美解决mysql客户端授权后连接失败的问题

在本地(192.168.1.152)部署好mysql环境,授权远程客户机192.168.1.%连接本机的mysql,在iptables防火墙也已开通3306端口。如下:mysql> select host,user,password from mysql.user;+--------------+-----------------+--------------------...

weixin_39913472的博客 646

mysql远程连接 Communications link failure失败问题

95: 2022-10-09 12:39:36 ERROR common_util.py common_util 导入数据失败。导致emr上多次重试提交到了没权限的服务器上 导致延迟几个小时 没成功,多次重试。需要把几台服务器的权限都加上。本地三台服务器 , 跑python导数脚本 结果 只有1台有权限, 另外两台不能连接这个mysql实例。这种情况 link communication 失败问题 很有可能是。

mayaohao的博客 1381

nacos连接mysql失败_完美解决MySQL通过localhost无法连接数据库的问题

问题:一台服务器的PHP程序通过localhost地址无法连接数据库,但是如果设置为127.0.0.1则可以正常连接,连接其他数据库服务器也正常。MySQL的权限设置正确,且通过mysql命令行客户端可以正常连接数据库。分析:这是典型的socket没有正确设置的情况。连接MySQL数据库有两种方式:TCP/IP(一般理解的端口的那种)和Unix套接字(一般叫socket或者sock)。大部分情况下...

weixin_34738099的博客 2551

docker配置mysql遇到的问题:网络连接超时、启动mysql失败、navicat无法远程连接mysql

在这一行-v /mydata/mysql/conf:/etc/mysql,我们将主机路径/mydata/mysql/conf挂载到容器路径/etc/mysql下,但实际上容器配置在/etc/mysql/conf.d/目录下。这个错误表明,你的客户端尝试通过 IP 地址连接到 MySQL 服务器,但服务器拒绝了这个请求。我们的命令指定的配置文件路径是/etc/mysql/,而容器读取的配置文件路径是/etc/mysql/conf.d/。切换到国内镜像源,如使用阿里云的Docker镜像加速器,以提高下载速度。

2201_75442471的博客 1482
上一篇: vue中的is
下一篇: jvm内存设置
评论
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
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