hibernate——两种查询方式

Hibernate createQuery查询传递参数的两种方式 采用问号?方式传参 @PersistenceContext private EntityManager entityManager; @Override public CustomApproval findApprovalById(Integer id) { // TODO Auto-generated method stub String sql = "select expiresAt... 阅读详情

一种是hql语句查询,用Query接口去实现;一种是条件查询,用Criteria接口去实现。这两种方式作用差不多,只是后者更符合面向对象一些。

用hql语句查询,如下例:

public static void query(String name){
		
		Session session=null;
		try{
			session=HibernateUtil.getSession();
			
			//String hql1="from User as user where user.name=?";
			//Query query=session.createQuery(hql1);
			//query.setString(0, hql1);
			
			String hql2="from User as user where user.name=:username";//使用命名参数
			Query query=session.createQuery(hql2);
			query.setString("username", name);
			
			query.setFirstResult(0);
			query.setMaxResults(10);//查询分页
			
			List<User> list = query.list();
			
			for(User user:list){
				System.out.println(user.getName());
			}
		}catch(HibernateException e){
			e.printStackTrace();
		}finally{
			if(session!=null){
				session.close();
			}
		}
	}


使用条件查询,如下例:

public static void criteria(String name){
		Session session=null;
		try{
			session=HibernateUtil.getSession();
			
			Criteria criteria=session.createCriteria(User.class);//条件查询
			criteria.add(Restrictions.eq("name", name));//增加约束条件,第一个参数是对象中的属性名,后一个是查询条件,eq就是equals的意思
			criteria.add(Restrictions.lt("birthday", new Date()));//lt是less than,即小于的意思
			
			List<User> list = criteria.list();
			
			//User user=(User)criteria.uniqueResult();//若查询结果只有一个的话,那么用这个方法也可以
			
			for(User user:list){
				System.out.println(user.getName()+" "+user.getBirthday());
			}
		}catch(HibernateException e){
			e.printStackTrace();
		}finally{
			if(session!=null){
				session.close();
			}
		}
	}


hibernate里面的 两种查询 hibernate里面的 两种查询 离线查询 和 HQL语句查询 立即下载

相关推荐

5分钟搞定!用Keil MDK将STM32F103C8T6工程无缝迁移到ZET6开发板

本文详细介绍了如何在Keil MDK环境下快速将STM32F103C8T6工程迁移到ZET6开发板,涵盖硬件差异对比、工程配置调整、外设适配及编译排错等关键步骤,帮助开发者5分钟内完成工程移植,大幅提升开发效率。

weixin_30879169的博客 409

hibernate的查询方式

hibernate的查询方式 1,hql(hibernate query language) 2,Native query 3,Criteria(条件) query // 使用Criteria方式查询 @Test public void testCriteria() throws Exception { Session session = sessionFactory.

java小兵 638

华为[ENSP]OSPF的配置实例(单区域+多区域)

华为[ENSP]OSPF的配置实例(单区域+多区域)

weixin_62594100的博客 2万+

hibernate的查询2个方式

注意其中设置查询条件参数的不同 方法一:通过Query查询 public List findCustomerVague(String cusname) {    s=HButil.getSession();    String hql="from Customer as customer where customer.cusname like:cusname";    Query que

flymoringbird的博客 435

jetson orin nano 使用cuPCL

jetson开发板上使用pcl时,正常使用的是cpu,拖慢了整体的效率。cuPCL有一些库用于使用CUDA处理点云。

weixin_42256401的博客 1020

国内优秀MES开源框架

一套springboot 开源框架 代码标准规范化。 界面展示 演示地址 http://wangziyangmes.pzzd.xyz 用户名admin 密码123 系统管理 工艺管理 计划管理 物料管理 *用户管理* *工艺路线管理* 工单下达...

q707231019的博客 2万+

hibernate的三种查询方式

Hibernate的查询方式 hibernate的查询方式常见的主要分为三种: HQL, QBC(命名查询), 以及使用原生SQL查询(SqlQuery) 一、HQL查询 • HQL(Hibernate Query Language)提供了丰富灵活的查询方式,使用HQL进行查询也是Hibernate官方推荐使用的查询方式。 • HQL在语法结构上和SQL语句十分的相同,所以可以很快的上手进...

weixin_39941298的博客 5296

别再用Excel硬算传热了!用MATLAB/Octave复现国赛数学建模A题(附完整代码与避坑指南)

本文详细介绍了如何利用MATLAB/Octave高效解决数学建模中的传热问题,替代传统的Excel计算方法。通过复现2018年国赛A题的高温作业服传热模型,从理论推导到代码实现,提供了完整的有限差分法解决方案和参数拟合技巧,帮助读者提升数学建模效率和准确性。

weixin_30512965的博客 379

Hibernate条件查询的两种方式

Hibernate条件查询的两种方式

God's blog 4100

STM32CubeIDE无法连接STLinkV2的一个可能原因

前些日子买了个新的STLINKV2 , ( 老的被我家猫猫一泡尿送上西天了)刚开始还挺正常的, 后来有一天就突然间发现无法调试了.网上尝试了各种方法, 各种操作. 百度了好久. 最后好在我自己研究出来了.我使用的是 STM32CubeIDE 这个开发工具调试选项如下图其它的不需要修改, 然后点击下面的apply 按钮, 然后调试Debug, 提示 No STLink 如下图后来尝试了下载ST-LINK Utility 用它是可以正常连接STLink的, 然后也能正常进行升级操作.

走错路的程序员 9829

108、AWB 时间滤波器:帧间白平衡平滑,防止色彩跳变与过冲

摘要: 文章探讨了AWB(自动白平衡)时间滤波器的设计与优化,以解决帧间白平衡跳变和过冲问题。通过自适应状态机(STABLE/TRANSITION/JUMP)动态调整平滑系数alpha,结合帧率归一化和过冲保护(方向检测+限幅),实现场景切换快速响应与静态稳定性的平衡。作者分享了工程经验:避免固定阈值、联动AE/AF、真实场景验证,并推荐简化卡尔曼滤波替代IIR。最终方案在旗舰机前置摄像头中成功消除“闪白”现象,强调参数需动态适应光照与色温变化。

sundong_81的博客 64

hibernate中Query条件查询两种方式

//方法一 Session session =sf.openSession(); Transaction tr =session.beginTransaction(); Query query=session.createQuery("from StudentBean where parentid=?"); query.setInteger(0,id); List&l...

Mr.Cheney的专栏 524

图神经网络实战(21)——图神经网络的可解释性

在本节中,我们探讨了应用于图神经网络 (Graph Neural Networks, GNN) 的可解释性人工智能 (Explainable Artificial Intelligence, XAI) 技术。可解释性是许多领域的关键要素,可以帮助我们建立更好的模型。我们介绍了不同的局部解释技术,并重点讨论了 GNNExplainer (基于扰动的方法),在图分类任务中应用 GNNExplainer。

盼小辉丶的博客 9543

hibernate createQuery查询传递参数的两种方式

方式一: String hql = "from InventoryTask it where it.orgId=:orgId"; Session session = getSession(); Query query=session.createQuery(hql); query.setString("orgId",orgId);

java_5666666的博客 5940

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...

SAP B1中文培訓教材_SDK

公司内部培训资料,对开发工具 ,DIAPI, UIAPI,Add-ON有详细解说

hibernate级联查询映射的两种方式

Hibernate主要支持两种查询方式:HQL查询和Criteria查询。前者应用较为广发,后者也只是调用封装好的接口。 现在有一个问题,就是实现多表连接查询,且查询结果集不与任何一个实体类对应,怎么解决呢? 举个例子: 现在有两个表,一个users用户表, 一个goods商品表,每个用户可以有多个商品,而一个商品只能对应一个用户。 users表中的字段:userId,userName,t...

weixin_30340775的博客 97

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")

hibernate多条件组合查询的两种方式

第一种:拼接hql语句方式: 1 //第一种方式:拼接hql语句实现 2 public List<Customer> findMoreCondition(Customer customer) { 3 //拼接hql语句 4 String hql = "from Customer where 1=1 "; 5 ...

weixin_33795093的博客 233

怀旧版电脑GBC模拟器

再现原版GBC掌机的界面,电脑运行连游戏界面都一样大小,很怀旧,很真实,(英文版)强烈推荐,很小的文件,很难才找到的。

Hibernate命令式SQL查询的两种方式

Hibernate命令式SQL查询的两种方式 SQLHibernate           *************************************************************************************************************** 上面一句执行的HQL语句命令    

松鼠的专栏 432

hibernate的sql查询出来封装实体的两种方式

1.在用hibernate的查询数据库时,即可以用hql,也可以用sql语句至于用hql语句查出来的结果可以是一个实体对象 如: public List getAll() { String sql = "from CraGovernment as o"; Query query = sessionFactory.getCurrentSession().createQuery(sql)

为梦想而奋斗,无悔_一叶扁舟的点滴成长! 7736

Hibernate 带参数查询的两种方式

1、使用?通配符 public User validate(String userName, String password) { String hql = "from User u where u.userName = ? and u.password = ?"; User user = null; List<User> ...

weixin_30338743的博客 421

Hibernate查询返回自定义VO的两种方式

说明:createQuery用的hql语句进行查询,createSQLQuery用sql语句查询; 前者以hibernate生成的Bean为对象装入list返回;后者则是以对象数组进行存储; 一、通过createSQLQuery()查询获得,代码如下: 1 String sql = "select g.*, b.name as bigTypeName, s.name as smallTy...

weixin_30485291的博客 513
上一篇: hibernate——简单的增删改查
下一篇: hibernate——多对一和一对多映射浅析
hackerain
博客等级 码龄16年 753粉丝 220原创
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