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 decided to do some followups.

A couple years ago, I wrote a blog post on greenlets, threads, and processes. At that time, it was already possible to write things in terms of explicit coroutines (Greg Ewing's original yield from proposal already had a coroutine scheduler as an example, Twisted already had @inlineCallbacks, and asyncio had even been added to the stdlib), but it wasn't in heavy use yet. Things have changed since then, especially with the addition of the async and await keywords to the language (and the popularity of similar constructs in a wide variety of other languages). So, it's time to take a look back (and ahead).

Differences

Automatic waiting

Greenlets are the same thing as coroutines, but greenlet libraries like gevent are not just like coroutine libraries like asyncio. The key difference is that greenlet libraries do the switching magically, while coroutine libraries make you ask for it explicitly.

For example, with gevent, if you want to yield until a socket is ready to read from and then read from the socket when waking up, you write this:
    buf = sock.recv(4096)
To do the same thing with asyncio, you write this:
    buf = await loop.sock_recv(sock, 4096)
Forget (for now) the difference in whether recv is a socket method or a function that takes a socket; the key difference is that await. In asyncio, any time you're going to wait for a value, yielding the processor to other coroutines until you're ready to run, you always do this explicitly, with await. In gevent, you just call one of the functions that automatically does the waiting for you.

In practice, while marking waits explicitly is a little harder to write (especially during quick and dirty prototyping), it seems to be harder to get wrong, and a whole lot easier to debug. And the more complicated things get, the more important this is.

If you miss an await, or try to do it in a non-async function, your code will usually fail hard with a obvious error message, rather than silently doing something undesirable.

Meanwhile, let's say you're using some shared container, and you've got a race on it, or a lock that's being held too long. It's dead simple to tell at a glance whether you have an await between a read and a write to that container, while with automatic waiting, you have to read every line carefully. Being able to follow control flow at a glance is really one of the main reasons people use Python in the first place, and await extends that ability to concurrent code.

Serial-style APIs

Now it's time to come back to the difference between sock.recv and sock_recv(sock). The asyncio library doesn't expose a socket API, it exposes an API that looks sort of similar to the socket API. And, if you look around other languages and frameworks, from JavaScript to C#, you'll see the same thing.

It's hard to argue that the traditional socket API is in any objective sense better, but if you've been doing socket programming for a decade or four, it's certainly more familiar. And there's a lot more language-agnostic documentation on how it works, both tutorial and reference (e.g., if you need to look up the different quirks of a function on Linux vs. *BSD, the closer you are to the core syscall, the easier it will be to find and understand the docs).

In practice, however, the vast majority of code in a nontrivial server is going to work at a higher level of abstraction. Most often, that abstraction will be Streams or Protocols or something similar, and you'll never even see the sockets. If not, you'll probably be building your own abstraction, and only the code on the inside—a tiny fraction of your overall code—will ever see the sockets.

One case where using the serial-style APIs really does help, however, is when you've got a mess of already-written code that's either non-concurrent or using threads or processes, and you want to convert it to use coroutines. Rewriting all that code around asyncio (no matter which level you choose) is probably a non-trivial project; rewriting it around gevent, you just import all the monkeypatches and you're 90% done. (You still need to scan your code, and test the hell out of it, to make sure you're not doing anything that will break or become badly non-optimal, of course, but you don't need to rewrite everything.)

Conclusion

If I were writing the same blog post today, I wouldn't recommend magic greenlets for most massively-concurrent systems; I'd recommend explicit coroutines instead.

There is still a place for gevent. But that place is largely in migrating existing threading-based (or on-concurrent) codebases. If you (and your intended collaborators) are familiar enough with threading and traditional APIs, it may still be worth considering for simpler systems. But otherwise, I'd strongly consider asyncio (or some other explicit coroutine framework) instead.
6

View comments

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
About Me
About Me
Loading
Dynamic Views theme. Powered by Blogger. Report Abuse.