动态网站的问题就在于它是动态的。 也就是说每次用户访问一个页面,服务器要执行数据库查询,启动模板,执行业务逻辑以及最终生成一个你所看到的网页,这一切都是动态即时生成的。 从处理器资源的角度来看,这是比较昂贵的。
对于大多数网络应用来说,过载并不是大问题。 因为大多数网络应用并不是washingtonpost.com或Slashdot;它们通常是很小很简单,或者是中等规模的站点,只有很少的流量。 但是对于中等至大规模流量的站点来说,尽可能地解决过载问题是非常必要的。
这就需要用到缓存了。
缓存的目的是为了避免重复计算,特别是对一些比较耗时间、资源的计算。 下面的伪代码演示了如何对动态页面的结果进行缓存。
given a URL, try finding that page in the cache if the page is in the cache: return the cached page else: generate the page save the generated page in the cache (for next time) return the generated page
为此,Django提供了一个稳定的缓存系统让你缓存动态页面的结果,这样在接下来有相同的请求就可以直接使用缓存中的数据,避免不必要的重复计算。 另外Django还提供了不同粒度数据的缓存,例如: 你可以缓存整个页面,也可以缓存某个部分,甚至缓存整个网站。
Django也和”上游”缓存工作的很好,例如Squid(http://www.squid-cache.org)和基于浏览器的缓存。 这些类型的缓存你不直接控制,但是你可以提供关于你的站点哪部分应该被缓存和怎样缓存的线索(通过HTTP头部)给它们
设定缓存
缓存系统需要一些少量的设定工作。 也就是说,你必须告诉它缓存的数据应该放在哪里,在数据库中,在文件系统,或直接在内存中。 这是一个重要的决定,影响您的高速缓存的性能,是的,有些类型的缓存比其它类型快。
缓存设置在settings文件的 CACHE_BACKEND中。 这里是一个CACHE_BACKEND所有可用值的解释。
内存缓冲
Memcached是迄今为止可用于Django的最快,最有效的缓存类型,Memcached是完全基于内存的缓存框架,最初开发它是用以处理高负荷的LiveJournal.com随后由Danga Interactive公司开源。 它被用于一些站点,例如Facebook和维基百科网站,以减少数据库访问,并大幅提高站点的性能。
Memcached是免费的(http://danga.com/memcached)。它作为一个守护进程运行,并分配了特定数量的内存。 它只是提供了添加,检索和删除缓存中的任意数据的高速接口。 所有数据都直接存储在内存中,所以没有对使用的数据库或文件系统的开销。
在安装了Memcached本身之后,你将需要安装Memcached Python绑定,它没有直接和Django绑定。 这两个可用版本。 选择和安装以下模块之一:
最快的可用选项是一个模块,称为cmemcache,在http://gijsbert.org/cmemcache。
如果您无法安装cmemcache,您可以安装python - Memcached,在ftp://ftp.tummy.com/pub/python-memcached/。如果该网址已不再有效,只要到Memcached的网站http://www.danga.com/memcached/),并从客户端API完成Python绑定。
若要使用Memcached的Django,设置CACHE_BACKEND到memcached:/ / IP:port/,其中IP是Memcached的守护进程的IP地址,port是Memcached运行的端口。
在这个例子中,Memcached运行在本地主机 (127.0.0.1)上,端口为11211:
CACHE_BACKEND = 'memcached://127.0.0.1:11211/'
Memcached的一个极好的特性是它在多个服务器间分享缓存的能力。 这意味着您可以在多台机器上运行Memcached的守护进程,该程序会把这些机器当成一个单一缓存,而无需重复每台机器上的缓存值。 要充分利用此功能,请在CACHE_BACKEND里引入所有服务器的地址,用分号分隔。
这个例子中,缓存在运行在IP地址为172.19.26.240和172.19.26.242,端口号为11211的Memcached实例间分享:
CACHE_BACKEND = 'memcached://172.19.26.240:11211;172.19.26.242:11211/'
这个例子中,缓存在运行在172.19.26.240(端口11211),172.19.26.242(端口11212),172.19.26.244(端口11213)的Memcached实例间分享:
CACHE_BACKEND = 'memcached://172.19.26.240:11211;172.19.26.242:11212;172.19.26.244:11213/'
最后有关Memcached的一点是,基于内存的缓存有一个重大的缺点。 由于缓存的数据存储在内存中,所以如果您的服务器崩溃,数据将会消失。 显然,内存不是用来持久化数据的,因此不要把基于内存的缓存作为您唯一的存储数据缓存。 毫无疑问,在Django的缓存后端不应该用于持久化,它们本来就被设计成缓存的解决方案。但我们仍然指出此点,这里是因为基于内存的缓存是暂时的。

Python is easier to learn and use, while C is more powerful but complex. 1. Python syntax is concise and suitable for beginners. Dynamic typing and automatic memory management make it easy to use, but may cause runtime errors. 2.C provides low-level control and advanced features, suitable for high-performance applications, but has a high learning threshold and requires manual memory and type safety management.

Python and C have significant differences in memory management and control. 1. Python uses automatic memory management, based on reference counting and garbage collection, simplifying the work of programmers. 2.C requires manual management of memory, providing more control but increasing complexity and error risk. Which language to choose should be based on project requirements and team technology stack.

Python's applications in scientific computing include data analysis, machine learning, numerical simulation and visualization. 1.Numpy provides efficient multi-dimensional arrays and mathematical functions. 2. SciPy extends Numpy functionality and provides optimization and linear algebra tools. 3. Pandas is used for data processing and analysis. 4.Matplotlib is used to generate various graphs and visual results.

Whether to choose Python or C depends on project requirements: 1) Python is suitable for rapid development, data science, and scripting because of its concise syntax and rich libraries; 2) C is suitable for scenarios that require high performance and underlying control, such as system programming and game development, because of its compilation and manual memory management.

Python is widely used in data science and machine learning, mainly relying on its simplicity and a powerful library ecosystem. 1) Pandas is used for data processing and analysis, 2) Numpy provides efficient numerical calculations, and 3) Scikit-learn is used for machine learning model construction and optimization, these libraries make Python an ideal tool for data science and machine learning.

Is it enough to learn Python for two hours a day? It depends on your goals and learning methods. 1) Develop a clear learning plan, 2) Select appropriate learning resources and methods, 3) Practice and review and consolidate hands-on practice and review and consolidate, and you can gradually master the basic knowledge and advanced functions of Python during this period.

Key applications of Python in web development include the use of Django and Flask frameworks, API development, data analysis and visualization, machine learning and AI, and performance optimization. 1. Django and Flask framework: Django is suitable for rapid development of complex applications, and Flask is suitable for small or highly customized projects. 2. API development: Use Flask or DjangoRESTFramework to build RESTfulAPI. 3. Data analysis and visualization: Use Python to process data and display it through the web interface. 4. Machine Learning and AI: Python is used to build intelligent web applications. 5. Performance optimization: optimized through asynchronous programming, caching and code

Python is better than C in development efficiency, but C is higher in execution performance. 1. Python's concise syntax and rich libraries improve development efficiency. 2.C's compilation-type characteristics and hardware control improve execution performance. When making a choice, you need to weigh the development speed and execution efficiency based on project needs.


Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

Video Face Swap
Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Article

Hot Tools

Atom editor mac version download
The most popular open source editor

SublimeText3 Linux new version
SublimeText3 Linux latest version

SublimeText3 Mac version
God-level code editing software (SublimeText3)

SublimeText3 English version
Recommended: Win version, supports code prompts!

SAP NetWeaver Server Adapter for Eclipse
Integrate Eclipse with SAP NetWeaver application server.