When asyncio was first proposed, many people (not so much on python-ideas, where Guido first suggested it, but on external blogs) had the same reaction: Doing the core reactor loop in Python is going to be way too slow. Something based on libev, like gevent, is inherently going to be much faster.

Then, when Python 3.4 came out, people started benchmarking it. For example, this benchmark shows that handling 100000 redis connections, 50 at a time, sending 3-byte messages, takes 3.12 seconds in asyncio vs. 4.55 in gevent.

And yet, I still see people refusing to believe those benchmarks, or their own benchmarks that they run to prove them wrong.

So, why isn't it slow? Isn't Python really slow at looping? Isn't libev a tightly-optimized reactor core?

The problem is that, as is so common, people are focusing on optimizing the wrong part of the code.

Under the covers, gevent and asyncio do similar things:
  • Call a function like epoll or kqueue to wait on a whole mess of sockets and see which ones are ready for input or output.
  • For each of those sockets:
    • Read the data.
    • Look up a coroutine in some kind of map.
    • Resume that coroutine.
    • Execute the actual Python code in that coroutine up to the next yield from/implicit suspend point.
Now, which parts of that would you expect libev to be able to optimize? Iterating over hundreds of sockets may be orders of magnitude slower in Python, but it's such a tiny percentage of the total time spent that it can't possibly matter. Remember, for each socket in that loop, you're going to make an I/O-bound syscall, do a context switch, and then interpret a bunch of Python code. It's going to be one of those things that takes time, and there's no way to optimize any of them by rewriting some completely irrelevant piece of code in (even tightly optimized) C.

From my own unscientific tests, using Fantix's work-in-progress port of gevent to Python 3 vs. asyncio, there's rarely a significant difference in either direction. I more often see a difference between Python 2.7 and 3.4. If I need to deal with lots of Unicode text, 3.4 is much faster; if I don't actually need to deal with it as Unicode but 3.x makes it hard for me to avoid doing so, 2.7 is much faster; if neither of those is relevant, 3.4 is sometimes significantly faster but sometimes not noticeably so.

There are of course sometimes good reasons to use gevent instead of asyncio. If you've got a bunch of threaded code that you want to convert over to using coroutines, for example, gevent makes it trivial. But using gevent it because you're absolutely sure that asyncio must be slow because Python code is slow, that's silly.
Posted by
0

Add a comment

It's been more than a decade since Typical Programmer Greg Jorgensen taught the word about Abject-Oriented Programming.

Much of what he said still applies, but other things have changed.

5

I haven't posted anything new in a couple years (partly because I attempted to move to a different blogging platform where I could write everything in markdown instead of HTML but got frustrated—which I may attempt again), but I've had a few private comments and emails on some of the old posts, so I

6

Looking before you leap

Python is a duck-typed language, and one where you usually trust EAFP ("Easier to Ask Forgiveness than Permission") over LBYL ("Look Before You Leap").

1

Background

Currently, CPython’s internal bytecode format stores instructions with no args as 1 byte, instructions with small args as 3 bytes, and instructions with large args as 6 bytes (actually, a 3-byte EXTENDED_ARG followed by a 3-byte real instruction).

6

If you want to skip all the tl;dr and cut to the chase, jump to Concrete Proposal.

8

Many people, when they first discover the heapq module, have two questions:

Why does it define a bunch of functions instead of a container type? Why don't those functions take a key or reverse parameter, like all the other sorting-related stuff in Python? Why not a type?

At the abstract level, it'

1

Currently, in CPython, if you want to process bytecode, either in C or in Python, it’s pretty complicated.

The built-in peephole optimizer has to do extra work fixing up jump targets and the line-number table, and just punts on many cases because they’re too hard to deal with.

3

One common "advanced question" on places like StackOverflow and python-list is "how do I dynamically create a function/method/class/whatever"? The standard answer is: first, some caveats about why you probably don't want to do that, and then an explanation of the various ways to do it when you reall

1

A few years ago, Cesare di Mauro created a project called WPython, a fork of CPython 2.6.4 that “brings many optimizations and refactorings”. The starting point of the project was replacing the bytecode with “wordcode”. However, there were a number of other changes on top of it.

1

Many languages have a for-each loop.

4

When the first betas for Swift came out, I was impressed by their collection design. In particular, the way it allows them to write map-style functions that are lazy (like Python 3), but still as full-featured as possible.

2

In a previous post, I explained in detail how lookup works in Python.

2

The documentation does a great job explaining how things normally get looked up, and how you can hook them.

But to understand how the hooking works, you need to go under the covers to see how that normal lookup actually happens.

When I say "Python" below, I'm mostly talking about CPython 3.5.

7

In Python (I'm mostly talking about CPython here, but other implementations do similar things), when you write the following:

def spam(x): return x+1 spam(3) What happens?

Really, it's not that complicated, but there's no documentation anywhere that puts it all together.

2

I've seen a number of people ask why, if you can have arbitrary-sized integers that do everything exactly, you can't do the same thing with floats, avoiding all the rounding problems that they keep running into.

2

In a recent thread on python-ideas, Stephan Sahm suggested, in effect, changing the method resolution order (MRO) from C3-linearization to a simple depth-first search a la old-school Python or C++.

1

Note: This post doesn't talk about Python that much, except as a point of comparison for JavaScript.

Most object-oriented languages out there, including Python, are class-based. But JavaScript is instead prototype-based.

1

About a year and a half ago, I wrote a blog post on the idea of adding pattern matching to Python.

I finally got around to playing with Scala semi-seriously, and I realized that they pretty much solved the same problem, in a pretty similar way to my straw man proposal, and it works great.

About a year ago, Jules Jacobs wrote a series (part 1 and part 2, with part 3 still forthcoming) on the best collections library design.

1

In three separate discussions on the Python mailing lists this month, people have objected to some design because it leaks something into the enclosing scope. But "leaks into the enclosing scope" isn't a real problem.

2

There's a lot of confusion about what the various kinds of things you can iterate over in Python. I'll attempt to collect definitions for all of the relevant terms, and provide examples, here, so I don't have to go over the same discussions in the same circles every time.

8

Python has a whole hierarchy of collection-related abstract types, described in the collections.abc module in the standard library. But there are two key, prototypical kinds. Iterators are one-shot, used for a single forward traversal, and usually lazy, generating each value on the fly as requested.

2

There are a lot of novice questions on optimizing NumPy code on StackOverflow, that make a lot of the same mistakes. I'll try to cover them all here.

What does NumPy speed up?

Let's look at some Python code that does some computation element-wise on two lists of lists.

2

When asyncio was first proposed, many people (not so much on python-ideas, where Guido first suggested it, but on external blogs) had the same reaction: Doing the core reactor loop in Python is going to be way too slow. Something based on libev, like gevent, is inherently going to be much faster.

Let's say you have a good idea for a change to Python.

1

There are hundreds of questions on StackOverflow that all ask variations of the same thing. Paraphrasing:

lst is a list of strings and numbers. I want to convert the numbers to int but leave the strings alone.

2

In Haskell, you can section infix operators. This is a simple form of partial evaluation. Using Python syntax, the following are equivalent:

(2*) lambda x: 2*x (*2) lambda x: x*2 (*) lambda x, y: x*y So, can we do the same in Python?

Grammar

The first form, (2*), is unambiguous.

1

Many people—especially people coming from Java—think that using try/except is "inelegant", or "inefficient". Or, slightly less meaninglessly, they think that "exceptions should only be for errors, not for normal flow control".

These people are not going to be happy with Python.

2

If you look at Python tutorials and sample code, proposals for new language features, blogs like this one, talks at PyCon, etc., you'll see spam, eggs, gouda, etc. all over the place.

Most control structures in most most programming languages, including Python, are subordinating conjunctions, like "if", "while", and "except", although "with" is a preposition, and "for" is a preposition used strangely (although not as strangely as in C…).

There are two ways that some Python programmers overuse lambda. Doing this almost always mkes your code less readable, and for no corresponding benefit.

1

Some languages have a very strong idiomatic style—in Python, Haskell, or Swift, the same code by two different programmers is likely to look a lot more similar than in Perl, Lisp, or C++.

There's an advantage to this—and, in particular, an advantage to you sticking to those idioms.

1

Python doesn't have a way to clone generators.

At least for a lot of simple cases, however, it's pretty obvious what cloning them should do, and being able to do so would be handy. But for a lot of other cases, it's not at all obvious.

5

Every time someone has a good idea, they believe it should be in the stdlib. After all, it's useful to many people, and what's the harm? But of course there is a harm.

3

This confuses every Python developer the first time they see it—even if they're pretty experienced by the time they see it:

>>> t = ([], []) >>> t[0] += [1] --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <stdin> in <module>()

11
Blog Archive
Blog Archive
About Me
About Me
My Photo
Loading
Dynamic Views theme. Powered by Blogger. Report Abuse.