This article mainly introduces the Pythoncrawler DNS parsingcachingmethod, combined with specific examples to analyze the related operating skills and notes of Python using the socket module to parse the DNS cache. Matter , friends in need can refer to
This article describes the Python crawler DNS resolution caching method with examples. Share it with everyone for your reference, the details are as follows:
Foreword:
This is the core code in the DNS resolution cache module in the Python crawler, It's last year's code, and it's now available for those who are interested to take a look.
Generally, the DNS resolution time of a domain name is between 10 and 60 milliseconds. This may seem insignificant, but for larger crawlers, this cannot be ignored. For example, if we want to crawl Sina Weibo, there are 10 million requests under the same domain name (which is not too much), so it takes between 100,000 and 600,000 seconds, which is only 86,400 seconds per day. In other words, DNS resolution alone takes several days. At this time, adding DNS resolution caching, the effect is obvious.
Put the code directly below, and the instructions are at the back.
Code:
# encoding=utf-8 # --------------------------------------- # 版本:0.1 # 日期:2016-04-26 # 作者:九茶<bone_ace@163.com> # 开发环境:Win64 + Python 2.7 # --------------------------------------- import socket # from gevent import socket _dnscache = {} def _setDNSCache(): """ DNS缓存 """ def _getaddrinfo(*args, **kwargs): if args in _dnscache: # print str(args) + " in cache" return _dnscache[args] else: # print str(args) + " not in cache" _dnscache[args] = socket._getaddrinfo(*args, **kwargs) return _dnscache[args] if not hasattr(socket, '_getaddrinfo'): socket._getaddrinfo = socket.getaddrinfo socket.getaddrinfo = _getaddrinfo
Description:
It’s actually nothing The difficulty is to save the cache in the socket to avoid repeated acquisition.
You can put the above code in a dns_cache.py file, and just call this _setDNSCache() method in the crawler framework
.
It should be noted that if you use the gevent coroutine and use mon<a href="http://www.php.cn/wiki/1051.html" target="_blank">key</a>.patch_<a href="http://www.php.cn/wiki/1483.html" target="_blank">all</a>()
, It should be noted that the crawler has switched to the socket in gevent at this time, and the DNS resolution cache module should also use the gevent socket.
The above is the detailed content of Detailed explanation of how Python crawler DNS resolves cache. For more information, please follow other related articles on the PHP Chinese website!

Python and C each have their own advantages, and the choice should be based on project requirements. 1) Python is suitable for rapid development and data processing due to its concise syntax and dynamic typing. 2)C is suitable for high performance and system programming due to its static typing and manual memory management.

Choosing Python or C depends on project requirements: 1) If you need rapid development, data processing and prototype design, choose Python; 2) If you need high performance, low latency and close hardware control, choose C.

By investing 2 hours of Python learning every day, you can effectively improve your programming skills. 1. Learn new knowledge: read documents or watch tutorials. 2. Practice: Write code and complete exercises. 3. Review: Consolidate the content you have learned. 4. Project practice: Apply what you have learned in actual projects. Such a structured learning plan can help you systematically master Python and achieve career goals.

Methods to learn Python efficiently within two hours include: 1. Review the basic knowledge and ensure that you are familiar with Python installation and basic syntax; 2. Understand the core concepts of Python, such as variables, lists, functions, etc.; 3. Master basic and advanced usage by using examples; 4. Learn common errors and debugging techniques; 5. Apply performance optimization and best practices, such as using list comprehensions and following the PEP8 style guide.

Python is suitable for beginners and data science, and C is suitable for system programming and game development. 1. Python is simple and easy to use, suitable for data science and web development. 2.C provides high performance and control, suitable for game development and system programming. The choice should be based on project needs and personal interests.

Python is more suitable for data science and rapid development, while C is more suitable for high performance and system programming. 1. Python syntax is concise and easy to learn, suitable for data processing and scientific computing. 2.C has complex syntax but excellent performance and is often used in game development and system programming.

It is feasible to invest two hours a day to learn Python. 1. Learn new knowledge: Learn new concepts in one hour, such as lists and dictionaries. 2. Practice and exercises: Use one hour to perform programming exercises, such as writing small programs. Through reasonable planning and perseverance, you can master the core concepts of Python in a short time.

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.


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