Database Connection Pooling with Tomcat

本文探讨了通过配置数据库连接池来提升应用性能的方法。详细介绍了如何设置最大连接数、空闲连接数及等待时间等参数,并调整容器配置以应对高并发场景。
Software object pooling is not a new concept. There are many scenarios where some type of object pooling technique is employed to improve application performance, concurrency, and scalability. After all, having your database code create a new Connection object on every client request is an expensive process. Moreover, with today's demanding applications, creating new connections for data access from scratch, maintaining them, and tearing down the open connection can lead to massive load on the server.

We can configure a maximum number of DB connections in the pool. Make sure you choose a maximum connection count large enough to handle all of your database connections--alternatively, you can set 0 for no limit. Further, we can set the maximum number of idle database connections to be retained in the pool. Set this value to -1 for no limit. The most optimal performance is attained when the pool in its steady state contains just enough connections to service all concurrent connection requests, without having to create new physical database connections at runtime. We can also specify the maximum time (in milliseconds) to wait for a database connection to become available, which in this example is 10 seconds. An exception is thrown if this timeout is exceeded. You can set this value to -1 to wait indefinitely. Please make sure your connector driver, such as mysql.jar, is placed inside the /common/lib directory of your Tomcat installation.

To achieve performance and high throughput, we also need to fine-tune the container to work under heavy traffic. Here's how we'll configure the Connector element for the maxProcessors and acceptCount parameters in the server.xml file:

<!--  Configuring the request and response endpoints -->
<Connector port="80" maxHttpHeaderSize="8192" maxProcessors="150"
maxThreads="150" minSpareThreads="25" maxSpareThreads="75"
enableLookups="false" redirectPort="8443" acceptCount="150"
connectionTimeout="20000" disableUploadTimeout="true" />

Configuring JNDI Reference

In order for JNDI to resolve the reference, we have to insert a <resource-ref> tag into the web.xml deployment descriptor file. We first begin by setting a <listener> tag for registering a ServletContextListener as shown below:


<listener>
<listener-class> com.onjava.dbcp.DBCPoolingListener</listener-class>
</listener>

<!-- This component has a dependency on an external resource-->
<resource-ref>
<description> DB Connection Pooling</description>
<res-ref-name> jdbc/TestDB</res-ref-name>
<res-type> javax.sql.DataSource</res-type>
<res-auth> Container</res-auth>
</resource-ref>

<servlet>
<servlet-name> EnrolledStudents</servlet-name>
<servlet-class> com.onjava.dbcp.CourseEnrollmentServlet</servlet-class>
<load-on-startup> 1</load-on-startup>
</servlet>

<servlet-mapping>
<servlet-name> EnrolledStudents</servlet-name>
<url-pattern> /enrollment.do</url-pattern>
</servlet-mapping>

This binding is vendor-specific, and every container has its own mechanism for setting data sources. Please note that this is just a declaration for dependency on an external resource, and doesn't create the actual resource. Comprehending the tags is pretty straightforward: this indicates to the container that the local reference name jdbc/TestDB should be set by the app deployer, and this should match with the resource name, as declared in server.xml file.


打算改天把这篇文章给翻译一下,留个记号先


MATLAB代码实现了一个基于多种智能优化算法优化RBF神经网络的回归预测模型,其核心是通过智能优化算法自动寻找最优的RBF扩展参数(spread),以提升预测精度。 1.主要功能 多算法优化RBF网络:使用多种智能优化算法优化RBF神经网络的核心参数spread。 回归预测:对输入特征进行回归预测,适用于连续值输出问题。 性能对比:对比不同优化算法在训练集和测试集上的预测性能,绘制适应度曲线、预测对比图、误差指标柱状图等。 2.算法步骤 数据准备:导入数据,随机打乱,划分训练集和测试集(默认7:3)。 数据归一化:使用mapminmax将输入和输出归一化到[0,1]区间。 标准RBF建模:使用固定spread=100建立基准RBF模型。 智能优化循环: 调用优化算法(从指定文件夹中读取算法文件)优化spread参数。 使用优化后的spread重新训练RBF网络。 评估预测结果,保存性能指标。 结果可视化: 绘制适应度曲线、训练集/测试集预测对比图。 绘制误差指标(MAE、RMSE、MAPE、MBE)柱状图。 十种智能优化算法分别是: GWO:灰狼算法 HBA:蜜獾算法 IAO:改进天鹰优化算法,改进①:Tent混沌映射种群初始化,改进②:自适应权重 MFO:飞蛾扑火算法 MPA:海洋捕食者算法 NGO:北方苍鹰算法 OOA:鱼鹰优化算法 RTH:红尾鹰算法 WOA:鲸鱼算法 ZOA:斑马算法
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