Java事务之二——失败的案例

Java事务失效的七种方式 在Java中,事务管理通常由Spring框架提供支持,通过使用@Transactional注解来声明事务。然而,在某些情况下,事务可能会失效,导致数据不一致或其他问题。以下是几种常见的Java事务失效情况及其原因和解决方法。 阅读详情

Java事务之二——失败的案例

本文转载自: http://www.cnblogs.com/davenkin/archive/2013/02/17/java-transaction-2.html,侵删。

  在本系列的上一篇文章中,我们讲到了Java事务处理的基本问题,并且讲到了Service层和DAO层,在本篇文章中,我们将以BankService为例学习一个事务处理失败的案例。

  请通过以下方式下载github源代码:

git clone https://github.com/davenkin/java_transaction_workshop.git

 

  BankService的功能为:某个用户有两个账户,分别为银行账户和保险账户,并且有各自的账户号,BankService的transfer方法从该用户的银行账户向保险账户转帐,两个DAO分别用于对两个账户表的存取操作。

  定义一个BankService接口如下:

复制代码
package davenkin;

public interface BankService {
    public void transfer(int fromId, int toId, int amount);
}
复制代码

 

  在两个DAO对象中,我们通过传入的同一个DataSource获得Connection,然后通过JDBC提供的API直接对数据库进行操作。

定义操作银行账户表的DAO类如下:

复制代码
package davenkin.step1_failure;

import javax.sql.DataSource;
import java.sql.Connection;
import java.sql.PreparedStatement;
import java.sql.ResultSet;
import java.sql.SQLException;

public class FailureBankDao {
    private DataSource dataSource;

    public FailureBankDao(DataSource dataSource) {
        this.dataSource = dataSource;
    }


    public void withdraw(int bankId, int amount) throws SQLException {
        Connection connection = dataSource.getConnection();
        PreparedStatement selectStatement = connection.prepareStatement("SELECT BANK_AMOUNT FROM BANK_ACCOUNT WHERE BANK_ID = ?");
        selectStatement.setInt(1, bankId);
        ResultSet resultSet = selectStatement.executeQuery();
        resultSet.next();
        int previousAmount = resultSet.getInt(1);
        resultSet.close();
        selectStatement.close();


        int newAmount = previousAmount - amount;
        PreparedStatement updateStatement = connection.prepareStatement("UPDATE BANK_ACCOUNT SET BANK_AMOUNT = ? WHERE BANK_ID = ?");
        updateStatement.setInt(1, newAmount);
        updateStatement.setInt(2, bankId);
        updateStatement.execute();

        updateStatement.close();
        connection.close();

    }
}
复制代码

 

  FailureBankDao的withdraw方法,从银行账户表(BANK_ACCOUNT)中帐号为bankId的用户账户中取出数量为amount的金额。

  采用同样的方法,定义保险账户的DAO类如下:

复制代码
package davenkin.step1_failure;

import javax.sql.DataSource;
import java.sql.Connection;
import java.sql.PreparedStatement;
import java.sql.ResultSet;
import java.sql.SQLException;

public class FailureInsuranceDao {
    private DataSource dataSource;

    public FailureInsuranceDao(DataSource dataSource){
        this.dataSource = dataSource;
    }

    public void deposit(int insuranceId, int amount) throws SQLException {
        Connection connection = dataSource.getConnection();
        PreparedStatement selectStatement = connection.prepareStatement("SELECT INSURANCE_AMOUNT FROM INSURANCE_ACCOUNT WHERE INSURANCE_ID = ?");
        selectStatement.setInt(1, insuranceId);
        ResultSet resultSet = selectStatement.executeQuery();
        resultSet.next();
        int previousAmount = resultSet.getInt(1);
        resultSet.close();
        selectStatement.close();


        int newAmount = previousAmount + amount;
        PreparedStatement updateStatement = connection.prepareStatement("UPDATE INSURANCE_ACCOUNT SET INSURANCE_AMOUNT = ? WHERE INSURANCE_ID = ?");
        updateStatement.setInt(1, newAmount);
        updateStatement.setInt(2, insuranceId);
        updateStatement.execute();

        updateStatement.close();
        connection.close();
    }
}
复制代码

 

  FailureInsuranceDao类的deposit方法向保险账户表(INSURANCE_ACCOUNT)存入amount数量的金额,这样在BankService中,我们可以先调用FailureBankDao的withdraw方法取出一定金额的存款,再调用FailureInsuranceDao的deposit方法将该笔存款存入保险账户表中,一切看似OK,实现BankService接口如下:

复制代码
package davenkin.step1_failure;

import davenkin.BankService;

import javax.sql.DataSource;
import java.sql.Connection;
import java.sql.SQLException;

public class FailureBankService implements BankService{
    private FailureBankDao failureBankDao;
    private FailureInsuranceDao failureInsuranceDao;
    private DataSource dataSource;

    public FailureBankService(DataSource dataSource) {
        this.dataSource = dataSource;
    }

    public void transfer(int fromId, int toId, int amount) {
        Connection connection = null;
        try {
            connection = dataSource.getConnection();
            connection.setAutoCommit(false);

            failureBankDao.withdraw(fromId, amount);
            failureInsuranceDao.deposit(toId, amount);

            connection.commit();
        } catch (Exception e) {
            try {
                assert connection != null;
                connection.rollback();
            } catch (SQLException e1) {
                e1.printStackTrace();
            }
        } finally {
            try
            {
                assert connection != null;
                connection.close();
            } catch (SQLException e)
            {
                e.printStackTrace();
            }
        }
    }

    public void setFailureBankDao(FailureBankDao failureBankDao) {
        this.failureBankDao = failureBankDao;
    }

    public void setFailureInsuranceDao(FailureInsuranceDao failureInsuranceDao) {
        this.failureInsuranceDao = failureInsuranceDao;
    }
}
复制代码

 

  在FailureBankService的transfer方法中,我们首先通过DataSource获得Connection,然后调用connection.setAutoCommit(false)已开启手动提交模式,如果一切顺利,则commit,如果出现异常,则rollback。 接下来,开始测试我们的BankService吧。

  为了准备测试数据,我们定义个BankFixture类,该类负责在每次测试之前准备测试数据,分别向银行账户(1111)和保险账户(2222)中均存入1000元。BankFixture还提供了两个helper方法(getBankAmount和getInsuranceAmount)帮助我们从数据库中取出数据以做数据验证。我们使用HSQL数据库的in-memory模式,这样不用启动数据库server,方便测试。BankFixture类定义如下:

复制代码
package davenkin;

import org.junit.Before;
import javax.sql.DataSource;
import java.sql.*;

public class BankFixture
{

    protected final DataSource dataSource = DataSourceFactory.createDataSource();

    @Before
    public void setUp() throws SQLException
    {
        Connection connection = dataSource.getConnection();
        Statement statement = connection.createStatement();

        statement.execute("DROP TABLE BANK_ACCOUNT IF EXISTS");
        statement.execute("DROP TABLE INSURANCE_ACCOUNT IF EXISTS");
        statement.execute("CREATE TABLE BANK_ACCOUNT (\n" +
                "BANK_ID INT,\n" +
                "BANK_AMOUNT INT,\n" +
                "PRIMARY KEY(BANK_ID)\n" +
                ");");

        statement.execute("CREATE TABLE INSURANCE_ACCOUNT (\n" +
                "INSURANCE_ID INT,\n" +
                "INSURANCE_AMOUNT INT,\n" +
                "PRIMARY KEY(INSURANCE_ID)\n" +
                ");");

        statement.execute("INSERT INTO BANK_ACCOUNT VALUES (1111, 1000);");
        statement.execute("INSERT INTO INSURANCE_ACCOUNT VALUES (2222, 1000);");

        statement.close();
        connection.close();
    }

    protected int getBankAmount(int bankId) throws SQLException
    {
        Connection connection = dataSource.getConnection();
        PreparedStatement selectStatement = connection.prepareStatement("SELECT BANK_AMOUNT FROM BANK_ACCOUNT WHERE BANK_ID = ?");
        selectStatement.setInt(1, bankId);
        ResultSet resultSet = selectStatement.executeQuery();
        resultSet.next();
        int amount = resultSet.getInt(1);
        resultSet.close();
        selectStatement.close();
        connection.close();
        return amount;
    }

    protected int getInsuranceAmount(int insuranceId) throws SQLException
    {
        Connection connection = dataSource.getConnection();
        PreparedStatement selectStatement = connection.prepareStatement("SELECT INSURANCE_AMOUNT FROM INSURANCE_ACCOUNT WHERE INSURANCE_ID = ?");
        selectStatement.setInt(1, insuranceId);
        ResultSet resultSet = selectStatement.executeQuery();
        resultSet.next();
        int amount = resultSet.getInt(1);
        resultSet.close();
        selectStatement.close();
        connection.close();
        return amount;
    }

}
复制代码

 

  编写的Junit测试继承自BankFixture类,测试代码如下:

复制代码
package davenkin.step1_failure;

import davenkin.BankFixture;
import org.junit.Test;
import java.sql.SQLException;
import static junit.framework.Assert.assertEquals;

public class FailureBankServiceTest extends BankFixture
{
    @Test
    public void transferSuccess() throws SQLException
    {
        FailureBankDao failureBankDao = new FailureBankDao(dataSource);
        FailureInsuranceDao failureInsuranceDao = new FailureInsuranceDao(dataSource);

        FailureBankService bankService = new FailureBankService(dataSource);
        bankService.setFailureBankDao(failureBankDao);
        bankService.setFailureInsuranceDao(failureInsuranceDao);

        bankService.transfer(1111, 2222, 200);

        assertEquals(800, getBankAmount(1111));
        assertEquals(1200, getInsuranceAmount(2222));

    }

    @Test
    public void transferFailure() throws SQLException
    {
        FailureBankDao failureBankDao = new FailureBankDao(dataSource);
        FailureInsuranceDao failureInsuranceDao = new FailureInsuranceDao(dataSource);

        FailureBankService bankService = new FailureBankService(dataSource);
        bankService.setFailureBankDao(failureBankDao);
        bankService.setFailureInsuranceDao(failureInsuranceDao);

        int toNonExistId = 3333;
        bankService.transfer(1111, toNonExistId, 200);

        assertEquals(1000, getInsuranceAmount(2222));
        assertEquals(1000, getBankAmount(1111));
    }
}
复制代码

 

  运行测试,第一个测试(transferSuccess)成功,第二个测试(transferFailure)失败。

  分析错误,原因在于:我们分别从FailureBankService,FailureBankDao和FailureInsuranceDao中调用了三次dataSource.getConnection(),亦即我们创建了三个不同的Connection对象,而Java事务是作用于Connection之上的,所以从在三个地方我们开启了三个不同的事务,而不是同一个事务。

  第一个测试之所以成功,是因为在此过程中没有任何异常发生。虽然在FailureBankService中将Connection的提交模式改为了手动提交,但是由于两个DAO使用的是各自的Connection对象,所以两个DAO中的Connection依然为默认的自动提交模式。

  在第二个测试中,我们给出一个不存在的保险账户id(toNonExistId),就是为了使程序产生异常,然后在assertion语句中验证两张表均没有任何变化,但是测试在第二个assertion语句处出错。发生异常时,银行账户中的金额已经减少,而虽然程序发生了rollback,但是调用的是FailureBankService中Connection的rollback,而不是FailureInsuranceDao中Connection的,对保险账户的操作根本就没有执行,所以保险账户中依然为1000,而银行账户却变为了800。

  因此,为了使两个DAO在同一个事务中,我们应该在整个事务处理过程中使用一个Connection对象,在下一篇文章中,我们将讲到通过共享Connection对象的方式达到事务处理的目的。

Java事务处理全解析(二)—— 失败的案例 在本系列的上一篇文章中,我们讲到了Java事务处理的基本问题,并且讲到了Service层和DAO层,在本篇文章中,我们将以BankService为例学习一个事务处理失败的案例。   请通过以下方式下载github源代码: git clone https://github.com/davenkin/java_transaction_workshop.git   Bank 阅读详情

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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]); 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