org.springframework.transaction.CannotCreateTransactionException: Failed to get user by id: 15002

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原创 最新推荐文章于 2026-05-07 09:10:53 发布 760 阅读 AI 写同款· GEO 优化 ›
HikariPool-1 - Connection is not available, request timed out after 6000ms Connection is not available, request timed out after 6000ms问题排查论证通过demo重现问题总结 问题 今天项目上频繁出现了大量的这样的报错 Caused by: java.sql.SQLTransientConnectionException: HikariPool-1 - Connection is not available, request timed out after 6000ms. at com.zaxxer.hikari.pool. 阅读详情

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🎉 一、错误日志

[2025-10-12 14:35:12.888] [ERROR] [org.springframework.transaction.CannotCreateTransactionException] - Failed to get user by id: 15002
 Caused by: java.sql.SQLTransientConnectionException: HikariPool-1 - Connection is not available, request timed out after 30000ms.
 Stack trace:
 org.springframework.jdbc.datasource.DataSourceTransactionManager.doBegin(DataSourceTransactionManager.java:309) ~[spring-jdbc-5.3.23.jar:5.3.23]
 org.springframework.transaction.support.AbstractPlatformTransactionManager.getTransaction(AbstractPlatformTransactionManager.java:378) ~[spring-tx-5.3.23.jar:5.3.23]
 org.springframework.transaction.interceptor.TransactionAspectSupport.createTransactionIfNecessary(TransactionAspectSupport.java:595) ~[spring-tx-5.3.23.jar:5.3.23]
 ... (72 frames omitted)
 Caused by: java.sql.SQLException: Connections could not be acquired from the underlying database!
 HikariPool.getConnection(HikariPool.java:195) ~[HikariCP-4.0.3.jar:na]
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 (1774) Hikari

优快云

博主分享

📥博主的人生感悟和目标

Java程序员廖志伟

📙经过多年在优快云创作上千篇文章的经验积累，我已经拥有了不错的写作技巧。同时，我还与清华大学出版社签下了四本书籍的合约，并将陆续出版。

面试备战资料

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场景描述链接
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RocketMQRocketMQ详解
KafkaKafka详解
RabbitMQRabbitMQ详解
MongoDBMongoDB详解
ElasticSearchElasticSearch详解
ZookeeperZookeeper详解
RedisRedis详解
MySQLMySQL详解
JVMJVM详解

集群部署（图文并茂，字数过万）

技术栈部署架构链接
MySQL使用Docker-Compose部署MySQL一主二从半同步复制高可用MHA集群Docker-Compose部署教程
Redis三主三从集群（三种方式部署/18个节点的Redis Cluster模式）三种部署方式教程
RocketMQDLedger高可用集群（9节点）部署指南
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项目名称链接地址
高并发红包雨项目https://gitee.com/java_wxid/red-packet-rain
微服务技术集成demo项目https://gitee.com/java_wxid/java_wxid

管理经验

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# HikariCP-4.0.3 # SQLTransientConnectionException # Timeout # HikariPool-1
版权声明：本文为博主原创文章，遵循 CC 4.0 BY-SA 版权协议，转载请附上原文出处链接和本声明。
HikariPool-1 - Connection is not available, request timed out after 30010ms.问题解决 在yml配置文件中设置Hikari的连接数，该连接数根据自己情况设定，切记不要设定太大，否则会导致其它工具（比如Navicat）无法连接数据库。，因为Hikari的默认连接数为10，我在批量导入时请求量太高导致数据库连接数不够，所以才导致该问题出现。无法为事务创建JDBC连接，HikariPool-1连接不可用。 阅读详情

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java.sql.SQLException: Connections could not be acquired from the underlying database!

java.sql.SQLException: Connections could not be acquired from the underlying database! at com.mchange.v2.sql.SqlUtils.toSQLException(SqlUtils.java:106) at com.mchange.v2.c3p0.impl.C3P0PooledCo...

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vue2，nuxt2，office，文档，文件，查看预览，弹框预览，下载预览，如何预览pdf文档，打开查看，office全套文档，实现在线预览文件，小程序内预览word文件，手机h5移动端网页，nuxt文档在线预览，预览office最好用的插件，下载查看，Office在线预览，vue-office，vue如何使用vue-office教程示例，word文档，ppt，exce表格，预览电子文件，文档无法解析，打不开，报错，失效，保存下载，vue前端预览并下载doc、xls、pdf、txt及图片，vue2中预览p

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解决无法获取数据库连接 java.sql.SQLException: Connections could not be acquired from the underlying database

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连接数据库出现：Connections could not be acquired from the underlying database

　　通常情况下，我们连接数据库时不会有大问题，今天遇到“Connections could not be acquired from the underlying database”，大意是不能连接当前所配置的数据库。出现这种问题,一般是配置参数有误。 　　因为我的数据库使用的是oracle数据库，前一段时间都是没有问题的，今天出现问题，程序配置对我来说是没有变化，最近几天的访问人数没...

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SIMATIC S7-1200, S7-1500 PID 控制[手册]

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解决Connections could not be acquired from the underlying database!

搭建ssm运行时报错 在用ssm搭建项目报了如下一系列错误,为远程连接 MySQL 8 时的错误 完整的错误信息 org.springframework.web.util.NestedServletException: Request processing failed; nested exception is org.mybatis.spring.MyBatisSystemException: nested exception is org.apache.ibatis.exceptions.Persis

qq_43104182的博客 7338

霍尼韦尔SoftMaster使用说明

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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&#39;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(&#39;DeepLearnToolbox&#39;));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(&#39;state&#39;,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&#39;); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand(&#39;state&#39;,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 = &#39;sigm&#39;;%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, &#39;Too big error&#39;);```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(&#39;state&#39;,0)sae = saesetup([784 100]);sae.ae{1}.activation_function = &#39;sigm&#39;;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)&#39;)% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = &#39;sigm&#39;;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, &#39;Too big error&#39;);```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x&#39;,28,28,60000))/255;test_x = double(reshape(test_x&#39;,28,28,10000))/255;train_y = double(train_y&#39;);test_y = double(test_y&#39;);%% 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&#39;ll get around 1.2% errorrand(&#39;state&#39;,0)cnn.layers = { struct(&#39;type&#39;, &#39;i&#39;) %input layer struct(&#39;type&#39;, &#39;c&#39;, &#39;outputmaps&#39;, 6, &#39;kernelsize&#39;, 5) %convolution layer struct(&#39;type&#39;, &#39;s&#39;, &#39;scale&#39;, 2) %sub sampling layer struct(&#39;type&#39;, &#39;c&#39;, &#39;outputmaps&#39;, 12, &#39;kernelsize&#39;, 5) %convolution layer struct(&#39;type&#39;, &#39;s&#39;, &#39;scale&#39;, 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, &#39;Too big error&#39;);```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(&#39;state&#39;,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, &#39;Too big error&#39;);%% ex2 neural net with L2 weight decayrand(&#39;state&#39;,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, &#39;Too big error&#39;);%% ex3 neural net with dropoutrand(&#39;state&#39;,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, &#39;Too big error&#39;);%% ex4 neural net with sigmoid activation functionrand(&#39;state&#39;,0)nn = nnsetup([784 100 10]);nn.activation_function = &#39;sigm&#39;; % 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, &#39;Too big error&#39;);%% ex5 plotting functionalityrand(&#39;state&#39;,0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = &#39;softmax&#39;; % 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, &#39;Too big error&#39;);%% 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(&#39;state&#39;,0)nn = nnsetup([784 20 10]); nn.output = &#39;softmax&#39;; % 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, &#39;Too big error&#39;);```[![Bitdeli Badge](https://d2weczhvl823v0.cloudfront.net/rasmusbergpalm/deeplearntoolbox/trend.png)](https://bitdeli.com/free "Bitdeli Badge")

Connections could not be acquired from the underly

&quot;Connections could not be acquired from the underlying database&quot; 大概意思是说:不能连接当前所配置的数据库. 出现这种问题,一般是配置参数有误.若远程连接mysql数据库的话,这时还要考虑你设置远程mysql.否则,也会出这个问题. 远程访问MySQL的设置 1.在User表中添加允许远程访问的用户 ...

Java 小试 925

Connections could not be acquired from the underlying database!原因及解决办法

1.  连接数据库参数配置是否正确 查看连接的url 用户名 密码是否正确 ,密码是否过期 2.  查看数据库server是否处于运行状态 如server没有启动 重启server 3.  检查到数据server的网络连接是否正常 4.  连接到数据库server的连接数是否已经达到最大连接数 1. 如已经达到最大连接数,检查程序是否使用连接后没有释放 2. 上调数据

ChinFeng的专栏 9415
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