httpclient超时总结

基于Matlab的自动泊车系统开发:利用模糊控制实现智能停车解决方案的详细教程 Matlab是一种广泛应用于科学计算、工程建模与仿真的编程语言,其强大的工具箱和简洁的编程语法,使其成为开发自动泊车系统的理想选择。在开始编写代码之前,我们需要搭建Matlab的工作环境,并熟悉相关的工具箱,如Fuzzy Logic Toolbox,用于实现模糊控制。首先,确保你已经安装了Matlab软件,并加载了必要的工具箱。接下来,我们将逐步讲解如何在Matlab中实现自动泊车系统。 阅读详情

Httpclient超时


背景:

网站这边多次因为httpclient调用超时时间没设置好导致关掉,影响非常不好,而且问题重复出现,查看网络,没有比较明确介绍httpclient所有超时相关的设置(大部分只提到连接超时(connectintimeout),读超时(sockettimeout),对连接池超时提到的比较少),因此本文对超时这块最介绍,其他功能性内容,apache官方提供了比较详细的解析,这里不做讨论。具体可见:http://hc.apache.org/httpcomponents-client-ga/tutorial/html/index.html

网站这边用的是:

<dependency>

<groupId>commons-httpclient</groupId>

<artifactId>commons-httpclient</artifactId>

<version>3.1</version>

</dependency>



配置:

privatefinalstaticMultiThreadedHttpConnectionManagermanager=newMultiThreadedHttpConnectionManager();

privatefinalstaticHttpClienthttpclient=newHttpClient(manager);

httpclient.setConnectionTimeout(1000);

httpclient.setTimeout(1000);



现象:

页面无法打开,堆栈信息如下:

Name: trhead-142

State: WAITING onorg.apache.commons.httpclient.MultiThreadedHttpConnectionManager$ConnectionPool@69a4cb

Total blocked: 0 Total waited: 1

Stack trace:

java.lang.Object.wait(Native Method)

org.apache.commons.httpclient.MultiThreadedHttpConnectionManager.doGetConnection(MultiThreadedHttpConnectionManager.java:518)

org.apache.commons.httpclient.MultiThreadedHttpConnectionManager.getConnectionWithTimeout(MultiThreadedHttpConnectionManager.java:416)

org.apache.commons.httpclient.HttpMethodDirector.executeMethod(HttpMethodDirector.java:153)

org.apache.commons.httpclient.HttpClient.executeMethod(HttpClient.java:397)

org.apache.commons.httpclient.HttpClient.executeMethod(HttpClient.java:323)

com.madding.test.MyRunnable.run(MyTest1.java:53)

java.lang.Thread.run(Thread.java:619)


大部分线程等待在:org.apache.commons.httpclient.MultiThreadedHttpConnectionManager.doGetConnection


分析:

问题:很明显连接池超时没设置,导致请求在线程池中等待,进而引起dubb无法处理其他请求。

解决:加httpclient.setHttpConnectionFactoryTimeout(1000);,设置连接池超时。


说明:废弃方法尽量不要用


进一步分析:

为什么没设置这个会导致请求等待:



MultiThreadedHttpConnectionManager代码在获取连接时去连接池取,而连接池在没设置超时timeToWait为0,即一直处于等待状态,如果没有notify不会结束等待。

试用

<dependency>

<groupId>org.apache.httpcomponents</groupId>

<artifactId>httpclient</artifactId>

<version>4.2</version>

</dependency>


发现代码中即使不设置线程池超时,在高并发下也能正常访问,查看代码,发现是因为在默认没设置线程池超时时把连接超时的时间作为线程超时时间,代码如下:



而且httpclient4的代码结构相对3做了很大重构,且实现比较优雅,可以考虑在本地私服把4.x版本添加进来。


完整的超时可参考如下:

staticPoolingClientConnectionManagerconnectionManager=null;

staticHttpClienthttpclient =null;

static{

connectionManager=newPoolingClientConnectionManager();

connectionManager.setMaxTotal(1);

httpclient=newDefaultHttpClient(connectionManager);

httpclient.getParams().setParameter("http.socket.timeout",1000);

httpclient.getParams().setParameter("http.connection.timeout",1000);

httpclient.getParams().setParameter("http.connection-manager.timeout",100000000L);

}




具体测试代码如下:

3.1:

packagecom.madding.test;


importorg.apache.commons.httpclient.DefaultMethodRetryHandler;

importorg.apache.commons.httpclient.HttpClient;

importorg.apache.commons.httpclient.HttpStatus;

importorg.apache.commons.httpclient.MultiThreadedHttpConnectionManager;

importorg.apache.commons.httpclient.methods.GetMethod;


/**

*@author madding.lip

*/

publicclassMyHttpClientTest3_1 {


privatefinalstaticMultiThreadedHttpConnectionManagermanager=newMultiThreadedHttpConnectionManager();

privatefinalstaticHttpClienthttpclient=newHttpClient(manager);


publicstaticvoidmain(String[] args) {

httpclient.getParams().setParameter("http.socket.timeout",1000);

httpclient.getParams().setParameter("http.connection.timeout",1000);

httpclient.getParams().setParameter("http.connection-manager.timeout",60*60L);

// httpclient.setConnectionTimeout(1000);

// httpclient.setTimeout(1000);

// httpclient.setHttpConnectionFactoryTimeout(1000);


for(inti = 0; true;i++) {

newThread(newMyRunnable(httpclient),"trhead-"+ i).start();

try{

Thread.sleep(100);

}catch(InterruptedException e) {

}

}

}


}


classMyRunnableimplementsRunnable {


HttpClienthttpclient=null;


MyRunnable(HttpClient client){

httpclient= client;

}


publicvoidrun() {

GetMethod getMethod =newGetMethod("http://www.apache.org/");


try{

DefaultMethodRetryHandlerretryHandler = newDefaultMethodRetryHandler();

retryHandler.setRetryCount(0);

getMethod.setMethodRetryHandler(retryHandler);

intstatusCode =httpclient.executeMethod(getMethod);

if(statusCode == HttpStatus.SC_OK){

getMethod.getResponseBodyAsString();

}

}catch(Exception e) {

System.err.println(e);

}finally{

getMethod.releaseConnection();

}

}

}



4.2:

packagecom.madding.test;


importjava.io.IOException;


importorg.apache.http.HttpEntity;

importorg.apache.http.HttpResponse;

importorg.apache.http.client.ClientProtocolException;

importorg.apache.http.client.HttpClient;

importorg.apache.http.client.methods.HttpGet;

importorg.apache.http.client.methods.HttpUriRequest;

importorg.apache.http.impl.client.DefaultHttpClient;

importorg.apache.http.impl.conn.PoolingClientConnectionManager;

importorg.apache.http.util.EntityUtils;


/**

*@author madding.lip

*/

publicclassMyHttpClientTest4_2 {

staticPoolingClientConnectionManagerconnectionManager=null;

staticHttpClienthttpclient =null;

static{

connectionManager=newPoolingClientConnectionManager();

connectionManager.setMaxTotal(1);

httpclient=newDefaultHttpClient(connectionManager);

httpclient.getParams().setParameter("http.socket.timeout",1000);

httpclient.getParams().setParameter("http.connection.timeout",1000);

httpclient.getParams().setParameter("http.connection-manager.timeout",100000000L);

}


publicstaticvoidmain(String[] args) {


for(inti = 0; true;i++) {

newThread(newMyTest(httpclient),"trhead-"+ i).start();

try{

Thread.sleep(100);

}catch(InterruptedException e) {

}

}

}

}


classMyTestimplementsRunnable {

staticHttpClienthttpclient =null;


publicMyTest(HttpClient hc){

httpclient= hc;

}


publicvoidrun() {

HttpUriRequest httpget =newHttpGet("http://www.apache.org/");

try{

HttpResponse response =httpclient.execute(httpget);


HttpEntity entity =response.getEntity();


System.out.println("----------------------------------------");

System.out.println(response.getStatusLine());

if(entity !=null){

System.out.println("Responsecontent length: " +entity.getContentLength());

EntityUtils.toString(entity);

//System.out.println(EntityUtils.toString(entity));

}

System.out.println("----------------------------------------");


}catch(ClientProtocolException e) {

System.err.println(e);

}catch(IOException e) {

System.err.println(e);

}finally{

if(httpget !=null){

httpget.abort();

}

}

}


}













LoRA 完整解析:从理论到实践 LoRA(低秩适应)是一种高效的大模型微调方法,通过添加少量可训练参数(低秩矩阵)来适配新任务,避免全参数微调的高成本。其核心是将权重更新ΔW分解为两个小矩阵B×A的乘积,大幅减少训练参数量(仅0.1-1%)。LoRA基于内在维度假设,通过低秩约束捕捉核心任务信息,兼具效率与效果。实际应用中,常用配置为秩r=16-32,目标模块选择注意力层的q_proj和v_proj,学习率比全参数微调大10倍(约1e-4)。优势包括参数高效(内存降低60-80%)、多任务适配灵活、保留预训练知识等,已成为大模型微调的主流 阅读详情

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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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