An overview of structural pattern matching for Python

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Python's match statement, which provides a long-sought C-like switch statement—though it is far more than that—has now been part of the language for more than six months. One of the authors of the series of Python Enhancement Proposals (PEPs) that described the feature, Brandt Bucher, came to PyCon 2022 in Salt Lake City, Utah to talk about the feature. He gave an overview of its history, some of its many-faceted abilities, a bit about how it was implemented, and some thoughts on its future, in a presentation on April 29, which was the first day of talks for the conference.

Bucher said that he was studying computer engineering when he encountered Python, which made him realize that he liked developing software a lot more than he liked designing hardware. He got involved in Python core development during that time and has been a core developer for the language for nearly two years at this point. He is now working for Microsoft on the Faster CPython team, though his biggest project to date has been the work on shepherding and implementing structural pattern matching (i.e. match), much of which was done while he was working for Research Affiliates.

History

The genesis of the PEP was a "nerd sniping email on a Wednesday during COVID" from Guido van Rossum to Bucher to see if he wanted to be part of the effort to try to add a match statement to the language. Bucher had been looking for a project, so he was definitely interested and joined the team of six authors working on what became PEP 622 ("Structural Pattern Matching"). It was, Bucher said, "a monster": a huge PEP that was not written with a clear audience in mind, which contained lots of extra bells and whistles that bloated the PEP (and feature).

The Python steering council diligently read the PEP, he said, and asked the authors to rewrite it so that the council could render a judgment on the feature. That resulted in three new PEPs to fully describe it:

• PEP 634: "Structural Pattern Matching: Specification"
• PEP 635: "Structural Pattern Matching: Motivation and Rationale"
• PEP 636: "Structural Pattern Matching: Tutorial"

Making that change improved the PEP, by splitting it into three pieces, each of which was aimed at a different audience. Bucher said that while PEP 634 is important, he hoped that the only people who read it were those who were implementing the feature. The other two are less dry and more approachable, he said; PEP 635 was specifically targeted at making the case to the council and other core developers. PEP 636 is one that everyone should read; it shows how to use match by walking through an example of a text-based adventure game.

The PEP authors used a dedicated GitHub repository for collaborating on the feature. That repository still exists and it makes a good record of the project going forward, he said. The decisions that were made and the reasons behind them are contained in the pull requests, issues, and so forth. During the process, several medium-sized applications using match were developed; as the feature changed during the process, that allowed the team to judge if a change truly improved the feature or not.

Not a switch statement

Bucher stressed, as he did multiple times in the discussions along the way, that the feature is not a switch statement. It looks a lot like a switch statement and can behave like one, but there is a lot more to it. By seeing it as only that, much of the power of structural pattern matching is lost. His goal in the talk was to convince attendees that the match feature is one that has far more uses and that it deserves a place in their code.

Structural pattern matching combines two features that Python developers are already familiar with: control flow and destructuring. Control flow is "just branching; we do this all the time". He showed two simple if-elif-else code snippets, one of which looked at the value of a particular object and the other that looked at the "shape" of the object by looking at its length. The control flow was changed based on tests of those two characteristics of the object.

Destructuring is simply pulling data out of an object, which can be done in various ways. One of his tests was accessing meal[0], which uses sequence unpacking, but there are other destructuring operations, such as extracting the key-value pairs from a dictionary or accessing arbitrary attributes on a Python object.

So structural pattern matching is allowing the programmer to branch while they destructure or destructure while they branch. The feature allows a "very powerful declarative programming style that creates a new way of viewing a lot of the same problems that we have been struggling with for a very long time". As he pointed out, though, he was roughly one-third of the way through the talk and had not actually shown a match statement, so he was about to change that.

He put up a slide with a simple example of the feature:

    match meal:
        case entrée, side:
	    ...

To clarify what the code is doing, he put up the equivalent code from Python 3.9:

    if isinstance(meal, Sequence) and len(meal) == 2:
	entrée, side = meal
	...

    case (entrée, side):
    case [entrée, side]:

The "_" is used as a wildcard pattern, which is like a capture pattern, in that it will match anything, but it does not bind any value, unlike a capture pattern. So, a pattern of "[_, side]" would match a two-element sequence but only bind side as if only meal[1] were extracted in the equivalent Python 3.9 code. A common idiom is to use "case _:" as the last case entry to serve as a catch-all. Each case is tried in turn; if none of the previous ones match, the catch-all will match and can, for example, raise an exception.

The other pattern type that Bucher described was value patterns, which just look like a literal value:

    case ["Spam", side]:
	...
    # equivalent to:
    if isinstance(meal, Sequence) and len(meal) == 2 and meal[0] == "Spam":
	side = meal[1]
	...

    case ["Spam" | "eggs", side]:
    case ["Spam", side] if not self.has_tried(side):
    case {"entrée": "Spam", "side": side}:
    case {"meal": ["Spam", side]}:
    case Meal(Food("Spam"), Food(side)):

Class patterns are what led to his "big 'a-ha' moment" with the feature. He was writing code for a red-black tree, which is a self-balancing binary tree; it is a data structure that he never fully understood, even though he had implemented one before. Implementing the operations for the tree (e.g. insertion, rebalancing, deletion, etc.) using class patterns "really really clarified how the logic worked" because it allowed him to see "the shape of the data" and the pieces he was working with as they were being rearranged.

"We didn't invent really any of this", Bucher said. Structural pattern matching has been around, mostly in functional programming languages, for more than 50 years. He showed a short function to recursively calculate the factorial of a number using match:

    def f(n: int) -> int:
        match n:
             case 0 | 1:
                 return 1
             case _:
                 return n * f(n - 1)

Implementation

He turned to the implementation of the feature, which he said took him around nine months, all of it during COVID lockdown. The match feature was able to take advantage of a new capability that comes with the PEG parser that is now used for CPython. The "match" and "case" strings are "soft keywords"; the parser is able to recognize when they are being used as keywords versus when they are used as regular identifiers. He cannot take credit for that part of the feature as Van Rossum implemented it as one of the first things that was done.

Soft keywords allowed the feature to be added without interfering with existing code that uses those names as identifiers, which is likely in plenty of code in the ecosystem. So they were able to add a big new feature, with a lot of surface area, that completely maintained backward compatibility. He showed an example of converting some code that uses those names, which nicely demonstrates that:

    ...
    match = re.match(PATTERN, "...")
    if match is not None:
	case, status = match
	if status == "closed":
	    ...

    # could become

    ...
    match = re.match(PATTERN, "...")
    match match:
	case case, "closed":
	    ...

He described a bit about the structural pattern matching compiler, which he likes to call the "SPaM compiler" for brevity's sake. He showed and compared the bytecode output for both versions of the first example above, starting with the Python 3.9 version. Even though both versions do the same thing, the version using match was noticeably shorter because it takes advantage of a new opcode (MATCH_SEQUENCE). That opcode was implemented by Mark Shannon and it does the same isinstance() check as the Python 3.9 code, but effectively does so as quickly as is possible by checking a flag on the CPython object and it is implemented in the CPython runtime. The bytecode also uses a new GET_LEN opcode, rather than pushing the name "len" onto the stack and then calling it; it just directly checks the length of the sequence in the CPython object.

That example shows the advantage of having dedicated syntax in the language for the match feature, Bucher said. The Python compiler has a lot of extra knowledge about exactly what you are trying to do within each case, so it can generate more efficient code. "This is the sort of thing that you only get from native syntax"; other techniques like transpiling to Python or using a Python package to achieve a similar effect cannot be as fast.

Future plans

He finished the talk by looking at some features that he is working on for the future; he has mostly working code for them, but they are not ready to be added quite yet. If he finds the time to finish them up, they may become part of Python 3.12 (which is due in October 2023). "The future is faster."

One area that he is "really excited about" is improved control flow. He gave an example:

    match meal:
	case ["Spam", side]:
	    print("Yay, Spam!")
	case ["eggs", side]:
	    print("Oh, eggs?")
	case [_, side]:
	    print("Hm, something else.")

This optimization uses a technique known as decision trees, which has a lot prior art and research done on it for languages like OCaml. It allows merging overlapping checks for adjacent patterns. "It is not too difficult to do; Python throws a couple of curve balls your way that make it non-trivial", but he has a 98% working version of it. It can apply to many different kinds of patterns beyond just the one he showed.

Something that falls out of the decision-tree work is improved reachability checks. If a particular case cannot be reached because what it would match is already matched by previous entries, CPython can detect that and give the developer a warning. For example:

    for number in range(100):
	match number % 5, number % 3:
	    case _, 0: print("Spam!")
	    case 0, _: print("Eggs?")
	    case 0, 0: print("Spam and eggs.")
	    case _, _: print(number)

That completed his talk, but he could not resist a joke at the end: "I heard there was great hiking in Salt Lake City but I did not realize it was inside the convention center." That was met with laughter and applause from the large, standing-room-only crowd of attendees. It was indeed something like a ten-minute walk to most of the track rooms from the keynotes and expo floor.

But, after two years of virtual PyCons, everyone seemed happy to return to something approaching normalcy. The vaccination and mask requirements helped to give attendees some level of comfort with maintaining their health at the event as well. Overall, this year's return to an in-person PyCon seemed like it was a rousing success.

[I would like to thank LWN subscribers for supporting my trip to Salt Lake City for PyCon.]