从事API相关的工作很有挑战性,在高峰期保持系统的稳定及健壮性就是其中之一,这也是我们在Mailgun做很多压力测试的原因。
这么久以来,我们已经尝试了很多种方法,从简单的ApacheBench到复杂些的自定义测试套。但是本贴讲述的,是一种使用python进行“快速粗糙”却非常灵活的压力测试的方法。
使用python写HTTP客户端的时候,我们都很喜欢用 Requests library。这也是我们向我们的API用户们推荐的。Requests 很强大,但有一个缺点,它是一个模块化的每线程一个调用的东西,很难或者说不可能用它来快速的产生成千上万级别的请求。
Treq on Twisted简介
为解决这个问题我们引入了Treq (Github库)。Treq是一个HTTP客户端库,受Requests影响,但是它运行在Twisted上,具有Twisted典型的强大能力:处理网络I/O时它是异步且高度并发的方式。
Treq并不仅仅限于压力测试:它是写高并发HTTP客户端的好工具,比如网页抓取。Treq很优雅、易于使用且强大。这是一个例子:
>>> from treq import get >>> def done(response): ... print response.code ... reactor.stop() >>> get("http://www.github.com").addCallback(done) >>> from twisted.internet import reactor 200
简单的测试脚本
如下是一个使用Treq的简单脚本,用最大可能量的请求来对单一URL进行轰炸。
#!/usr/bin/env python from twisted.internet import epollreactor epollreactor.install() from twisted.internet import reactor, task from twisted.web.client import HTTPConnectionPool import treq import random from datetime import datetime req_generated = 0 req_made = 0 req_done = 0 cooperator = task.Cooperator() pool = HTTPConnectionPool(reactor) def counter(): '''This function gets called once a second and prints the progress at one second intervals. ''' print("Requests: {} generated; {} made; {} done".format( req_generated, req_made, req_done)) # reset the counters and reschedule ourselves req_generated = req_made = req_done = 0 reactor.callLater(1, counter) def body_received(body): global req_done req_done += 1 def request_done(response): global req_made deferred = treq.json_content(response) req_made += 1 deferred.addCallback(body_received) deferred.addErrback(lambda x: None) # ignore errors return deferred def request(): deferred = treq.post('http://api.host/v2/loadtest/messages', auth=('api', 'api-key'), data={'from': 'Loadtest <test@example.com>', 'to': 'to@example.org', 'subject': "test"}, pool=pool) deferred.addCallback(request_done) return deferred def requests_generator(): global req_generated while True: deferred = request() req_generated += 1 # do not yield deferred here so cooperator won't pause until # response is received yield None if __name__ == '__main__': # make cooperator work on spawning requests cooperator.cooperate(requests_generator()) # run the counter that will be reporting sending speed once a second reactor.callLater(1, counter) # run the reactor reactor.run()
输出结果:
2013-04-25 09:30 Requests: 327 generated; 153 sent; 153 received 2013-04-25 09:30 Requests: 306 generated; 156 sent; 156 received 2013-04-25 09:30 Requests: 318 generated; 184 sent; 154 received
“Generated”类的数字代表被Twisted反应器准备好但是还没有发送的请求。这个脚本为了简洁性忽略了所有错误处理。为它添加超时状态的信息就留给读者作为一个练习。
这个脚本可以当做是一个起始点,你可以通过拓展改进它来自定义特定应用下的处理逻辑。建议你在改进的时候用collections.Counter 来替代丑陋的全局变量。这个脚本运行在单线程上,想通过一台机器压榨出最大量的请求的话,你可以用类似 mulitprocessing 的技术手段。
愿你乐在压力测试!

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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