bosh comet ajax

本文介绍了BOSH(双向流过同步HTTP)和Comet两种技术。BOSH是一种高效的双向HTTP传输协议,避免了轮询并减少了延迟,在即时通讯应用中广泛使用。Comet则允许服务器主动推送数据到浏览器,通过长时间保持HTTP连接实现这一目标。

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Bidirectional-streams Over Synchronous HTTP (BOSH) is a transport protocol that emulates a bidirectional stream between two entities (such as a client and a server) by using multiple synchronous HTTP request/response pairs without requiring the use of polling or asynchronouschunking.

For applications that require both “push” and “pull” communications, BOSH is significantly more bandwidth-efficient and responsive than most other bidirectional HTTP-based transport protocols and the techniques known as AJAX. BOSH achieves this efficiency and low latency by avoiding HTTP polling, yet it does so without resorting to chunked HTTP responses as is done in the technique known as Comet. To date, BOSH has been used mainly as a transport for traffic exchanged between Jabber/XMPP clients and servers (e.g., to facilitate connections from web clients and from mobile clients on intermittent networks). However, BOSH is not tied solely to XMPP and can be used for other kinds of traffic, as well.

For "push", a BOSH client starts an HTTP request, but the server postpones sending a reply until it has data to send.[1] After receiving a reply, the client immediately makes another request on the same HTTP connection, so the server can always send data to the client without waiting for the client to poll. If while waiting for a reply the client needs to send data to the server, it opens a second HTTP connection. There are at most two HTTP connections open at a time, one on which the server can send data as a reply and one on which the client can send data as a POST.

It is a draft standard of the XMPP Standards Foundation.

The related standard XMPP Over BOSH defines how BOSH may be used to transport XMPP stanzas. The result is an HTTP binding for XMPP communications that is intended to be used in situations where a device or client is unable to maintain a long-lived TCP connection to an XMPP server.




Comet is a web application model in which a long-held HTTP request allows a web server to push data to a browser, without the browser explicitly requesting it.[1][2] Comet is an umbrella term, encompassing multiple techniques for achieving this interaction. All these methods rely on features included by default in browsers, such as JavaScript, rather than on non-default plugins. The Comet approach differs from the original model of the web, in which a browser requests a complete web page at a time.[3]

The use of Comet techniques in web development predates the use of the word Comet as a neologism for the collective techniques. Comet is known by several other names, including Ajax Push,[4][5] Reverse Ajax,[6] Two-way-web,[7] HTTP Streaming,[7] and HTTP server push[8] among others.[9]




Ajax (also AJAX/ˈæks/; an acronym for Asynchronous JavaScript and XML)[1] is a group of interrelated web development techniques used on the client-side to create asynchronous web applications. With Ajax, web applications can send data to, and retrieve data from, a serverasynchronously (in the background) without interfering with the display and behavior of the existing page. Data can be retrieved using the XMLHttpRequest object. Despite the name, the use of XML is not required (JSON is often used instead. See AJAJ), and the requests do not need to be asynchronous.[2]

Ajax is not a single technology, but a group of technologies. HTML and CSS can be used in combination to mark up and style information. The DOM is accessed with JavaScript to dynamically display, and allow the user to interact with, the information presented. JavaScript and the XMLHttpRequest object provide a method for exchanging data asynchronously between browser and server to avoid full page reloads






内容概要:本文介绍了基于Python实现的SSA-GRU(麻雀搜索算法优化门控循环单元)时间序列预测项目。项目旨在通过结合SSA的全局搜索能力和GRU的时序信息处理能力,提升时间序列预测的精度和效率。文中详细描述了项目的背景、目标、挑战及解决方案,涵盖了从数据预处理到模型训练、优化及评估的全流程。SSA用于优化GRU的超参数,如隐藏层单元数、学习率等,以解决传统方法难以捕捉复杂非线性关系的问题。项目还提供了具体的代码示例,包括GRU模型的定义、训练和验证过程,以及SSA的种群初始化、迭代更新策略和适应度评估函数。; 适合人群:具备一定编程基础,特别是对时间序列预测和深度学习有一定了解的研究人员和技术开发者。; 使用场景及目标:①提高时间序列预测的精度和效率,适用于金融市场分析、气象预报、工业设备故障诊断等领域;②解决传统方法难以捕捉复杂非线性关系的问题;③通过自动化参数优化,减少人工干预,提升模型开发效率;④增强模型在不同数据集和未知环境中的泛化能力。; 阅读建议:由于项目涉及深度学习和智能优化算法的结合,建议读者在阅读过程中结合代码示例进行实践,理解SSA和GRU的工作原理及其在时间序列预测中的具体应用。同时,关注数据预处理、模型训练和优化的每个步骤,以确保对整个流程有全面的理解。
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