Throughput differences when using coroutines vs threading

A few days ago I has asked a question on SO about helping me design a paradigm for structuring multiple HTTP requests Here's the scenario. I would like a have a multi-producer, multi-consumer system. My producers crawl and scrape a few sites and add the links that it finds into a queue. Since I'll be crawling multiple sites, I would like to have multiple producers/crawlers. The con

使用协程和线程时的吞吐量差异

几天前,我问了一个关于如何帮助我设计构建多个HTTP请求的范例的问题 这是场景。 我希望有一个多生产者,多消费者体系。 我的制作人员抓取并抓取几个网站,并将其找到的链接添加到队列中。 由于我要抓取多个网站,因此我希望有多个生产者/抓取工具。 消费者/员工馈送此队列,向这些链接发出TCP / UDP请求,并将结果保存到我的Django数据库。 由于每个队列项目是完全独立的,因此我还想拥有多个工作人员。 人们建议使用

How do coroutines in Python compare to those in Lua?

Support for coroutines in Lua is provided by functions in the coroutine table, primarily create , resume and yield . The developers describe these coroutines as stackful, first-class and asymmetric. Coroutines are also available in Python, either using enhanced generators (and yield from ) or, added in version 3.5, async and await . How do coroutines in Python compare to those in Lua? Are t

Python中的协程与Lua中的协程相比如何?

Lua中的coroutine支持由coroutine表中的函数提供,主要是create , resume和yield 。 开发人员将这些协同程序描述为堆栈式,一流的和不对称的。 协程也可以在Python中使用,或者使用增强型生成器(并yield from生成),或者在3.5版本中添加async并await 。 Python中的协程与Lua中的协程相比如何? 他们也是堆叠式的,一流的和不对称的吗? 为什么Python需要如此多的结构( async def , async with , async for ,异步

Why having a syntaxless string or integer doesn't throw syntax error?

I honestly must be missing something. So I have this nice syntax. "Hello darling" #THIS! print "Hello world!" Why the hello darling doesn't throw syntax error. I mean, there's no print statement. The "Hello world" just sits there. And from the syntax highlight, the hello world is a string, not a comment. This also works. 7 print "8" And 7 is obviously an integer. The

为什么使用无语法的字符串或整数不会抛出语法错误?

我真的一定会错过一些东西。 所以我有这个很好的语法。 "Hello darling" #THIS! print "Hello world!" 为什么你好亲爱的不会抛出语法错误。 我的意思是,没有print声明。 “Hello world”就在那里。 从语法突出来看,hello world是一个字符串,而不是注释。 这也适用。 7 print "8" 而7显然是一个整数。 7只是坐在那里,没有引用一个变量,就在那里。 “Syntaxless” 问题:为什么它有效? 7和“Hello Darling”都是整

Efficient way to build a data set from fits image

I have a set of fits images: about 32000 images with resolution (256,256). The dataset that i've to build is matrix like, so the output shape is (32000, 256*256). The simple solution is a for loop, samething like: #file_names is a list of paths samples=[] for file_name in file_names: hdu=pyfits.open(file_name) samples.append(hdu[0].data.flatten()) hdu.close() #then i can use n

从fit图像构建数据集的有效方法

我有一组拟合图像:大约32000张图像的分辨率(256,256)。 我要建立的数据集是矩阵状的,所以输出形状是(32000,256 * 256)。 简单的解决方案是for循环,就像: #file_names is a list of paths samples=[] for file_name in file_names: hdu=pyfits.open(file_name) samples.append(hdu[0].data.flatten()) hdu.close() #then i can use numpy.concatenate to have a numpy ndarray 这个解决方案非常非常慢。

clean up re.match objects

This loop is used in barcode scanning software. It may run as many times as a barcode is scanned, which is hundreds of times in an hour. # locpats is a list of regular expression patterns of possible depot locations for pat in locpats: q = re.match(pat, scannedcode) if q: print(q) return True q is a Match object. The print(q) tells me that every match object gets its

清理重新匹配的对象

该循环用于条形码扫描软件。 它可以像扫描条码一样多次运行,一小时内可以运行数百次。 # locpats is a list of regular expression patterns of possible depot locations for pat in locpats: q = re.match(pat, scannedcode) if q: print(q) return True q是一个Match对象。 print(q)告诉我每个匹配对象都有自己的一小段内存。 他们会加起来。 我不知道总数是多少。 在if我不再需要Match对

Where builtin functions are implemented

I tried to look around but I couldn't find anything clear about this topic. Are built-in functions implemented in a module that is automatically imported every time Python is launched? In the case which is the module? Or are built-in functions just embedded functions inside the Python interpreter? For CPython, the built-in functions are (for the most part) implemented in the bltinmodul

内置函数的实现

我试图环顾四周,但我无法找到有关此主题的任何明确信息。 每次启动Python时都会自动导入模块中实现的内置函数吗? 在这个模块是什么情况下? 或者内置的函数只是嵌入Python解释器中的函数? 对于CPython,内置函数(大部分)在bltinmodule.c文件中实现。 例外主要是类型; 像str和dict和list类的东西在C源的Objects目录中有它们自己的C文件; 这些在bltinmodule源文件中列为表格。 从技术上讲,这被视为一个单独的模

Class vs. Type in Python

I just recently started to teach myself how to code. I am currently reading Think Python 2 for python 3 and when it teaches about the type() function, it gives the example type(2) which outputs <class 'int'> . It then states that "the word 'class' is used in the sense of a category; a type is a category of values." The part that confuses me is that the type() f

Python中的类与类型

我最近刚开始自学如何编码。 我目前正在阅读Think Python 2 for python 3,当它讲述type()函数时,它给出了输出<class 'int'>的示例type(2) 。 然后它说:“类'这个词是用于某个类别的意思;类型是一类值。” 迷惑我的部分是type()函数输出类而不是类型。 另外,我不确定类型和类之间的区别。 是类型“值”的字符串,浮点数和整数类,还是它们是同一个东西? 我已经看过这个,但找不到我的具体问题的答案或

Why do Python yield statements form a closure?

I have two functions that return a list of functions. The functions take in a number x and add i to it. i is an integer increasing from 0-9. def test_without_closure(): return [lambda x: x+i for i in range(10)] def test_with_yield(): for i in range(10): yield lambda x: x+i I would expect test_without_closure to return a list of 10 functions that each add 9 to x since i '

为什么Python yield语句形成闭包?

我有两个函数返回一个函数列表。 该功能采取了一些x ,并添加i给它。 i是一个从0-9增加的整数。 def test_without_closure(): return [lambda x: x+i for i in range(10)] def test_with_yield(): for i in range(10): yield lambda x: x+i 我希望test_without_closure返回一个10个函数的列表,每个函数都将9添加到x因为i的值是9 。 print sum(t(1) for t in test_without_closure()) # prints 100 我

Is there a need to close files that have no reference to them?

As a complete beginner to programming, I am trying to understand the basic concepts of opening and closing files. One exercise I am doing is creating a script that allows me to copy the contents from one file to another. in_file = open(from_file) indata = in_file.read() out_file = open(to_file, 'w') out_file.write(indata) out_file.close() in_file.close() I have tried to shorten this code and

是否需要关闭不参考它们的文件?

作为一个完整的编程初学者,我想了解打开和关闭文件的基本概念。 我正在做的一个练习是创建一个脚本,允许我将内容从一个文件复制到另一个文件。 in_file = open(from_file) indata = in_file.read() out_file = open(to_file, 'w') out_file.write(indata) out_file.close() in_file.close() 我试图缩短这段代码,并提出了这个问题: indata = open(from_file).read() open(to_file, 'w').write(indata) 这对我来说看起

Creating a new dict in Python

I want to build a dictionary in Python. However, all the examples that I see are instantiating a dictionary from a list, etc . .. How do I create a new empty dictionary in Python? Call dict with no parameters new_dict = dict() or simply write new_dict = {} 你可以这样做x = {} x['a'] = 1 了解如何编写预设字典对于了解以下内容也很有用: cmap = {'US':'USA','GB':'Great Britain'} def cxlate(co

在Python中创建一个新的字典

我想用Python构建一本字典。 然而,我看到的所有例子都是从列表中实例化一个字典等。 .. 如何在Python中创建一个新的空字典? 调用没有参数的dict new_dict = dict() 或者干脆写 new_dict = {} 你可以这样做x = {} x['a'] = 1 了解如何编写预设字典对于了解以下内容也很有用: cmap = {'US':'USA','GB':'Great Britain'} def cxlate(country): try: ret = cmap[country] except: ret = '?'