You spent so much time trying to create (preferably) one obvious way to do things. It seems like you’re of the opinion that doing things that way, the Python way, really lets you take advantage of Python.
Guido: I’m not sure that I really spend a lot of time making sure that there’s only one way. The “Zen of Python” is much younger than the language Python, and most defining characteristics of the language were there long before Tim Peters wrote it down as a form of poetry. I don’t think he expected it to be quite as widespread and successful when he wrote it up.
It’s a catchy phrase.
Guido: Tim has a way with words. “There’s only one way to do it” is actually in most cases a white lie. There are many ways to do data structures. You can use tuples and lists. In many cases, it really doesn’t matter that much whether you use a tuple or a list or sometimes a dictionary. It turns out usually if you look really carefully, one solution is objectively better because it works just as well in a number of situations, and there’s one or two cases where lists just works so much better than tuples when you keep growing them.
That comes more actually from the original ABC philosophy that was trying to be very sparse in the components. ABC actually shared a philosophy with ALGOL-68, which is now one of the deadest languages around, but was very influentia. Certainly where I was at the time during the 80s, it was very influential because Adriaan van Wijngaarden was the big guy from ALGOL 68. He was still teaching classes when I went to college. I did one or two semesters where he was just telling anecdotes from the history of ALGOL 68 if he felt like it. He had been the director of CWI. Someone else was it by the time I joined. There were many people who had been very close with ALGOL 68. I think Lambert Meertens, the primary author of ABC, was also one of the primary editors of the ALGOL 68 report, which probably means he did a lot of the typesetting, but he may occasionally also have done quite a lot of the thinking and checking. He was clearly influenced by ALGOL 68’s philosophy of providing constructs that can be combined in many different ways to produce all sorts of different data structures or ways of structuring a program.
It was definitely his influence that said, “We have lists or arrays, and they can contain any kind of other thing. They can contain numbers or strings, but they can also contain other arrays and tuples of other things. You can combine all of these things together.” Suddenly you don’t need a separate concept of a multidimensional array because an array of arrays solves that for any dimensionality. That philosophy of taking a few key things that cover different directions of flexibility and allow them to be combined was very much a part of ABC. I borrowed all of that almost without thinking about it very hard.
While Python tries to give the appearance that you can combine things in very flexible ways as long as you don’t try to nest statements inside expressions, there is actually a remarkable number of special cases in the syntax where in some cases a comma means a separation between parameters, and in other cases the comma means the items of a list, and in yet another case it means an implicit tuple.
There are a whole bunch of variations in the syntax where certain operators are not allowed because they would conflict with some surrounding syntax. That is never really a problem because you can always put an extra pair of parentheses around something when it doesn’t work. Because of that the syntax, at least from the parser author’s perspective, has grown quite a bit. Things like list comprehensions and generator expressions are syntactically still not completely unified. In Python 3000, I believe they are. There’s still some subtle semantic differences, but the syntax at least is the same.
Does the parser get simpler in Python 3000?
No more complex I think is a win.
Why the simplest, dumbest compiler imaginable?
Guido: That was originally a very practical goal, because I didn’t have a degree in code generation. There was just me, and I had to have the byte code generator behind me before I could do any other interesting work on the language.
I still believe that having a very simple parser is a good thing; after all, it is just the thing that turns the text into a tree that represents the structure of the program. If the syntax is so ambiguous that it takes really advanced parts of technology to figure it out, then human readers are probably confused half the time as well. It also makes it really hard to write another parser.
Python is incredibly simple to parse, at least at the syntactic level. At the lexical level, the analysis is relatively subtle because you have to read the indentation with a little stack that is embedded in the lexical analyzer, which is a counterexample for the theory of separation between lexical and grammatical analysis. Nevertheless, that is the right solution. The funny thing is that I love automatically generated parsers, but I do not believe very strongly in automatically generated lexical analysis. Python has always had a manually generated scanner and an automated parser.
People have written many different parsers for Python. Even port of Python to a different virtual machine, whether Jython or IronPython or PyPy, has its own parser, and it’s no big deal because the parser is never a very complex piece of the project, because the structure of the language is such that you can very easily parse it with the most basic one-token lookahead recursive descent parser.
What makes parsers slow is actually ambiguities that can only be resolved by looking ahead until the end of the program. In natural languages there are many examples where it’s impossible to parse a sentence until you’ve read the last word and the arbitrary nesting in the sentence. Or there are sentences that can only be parsed if you actually know the person that they are talking about, but that’s a completely different situation. For parsing programming languages, I like my one-token lookahead.
That suggests to me that there may never be macros in Python because you have to perform another parsing phase then!
Guido: There are ways of embedding the macros in the parser that could probably work. I’m not at all convinced that macros solve any problem that is particularly pressing for Python, though. On the other hand, since the language is easy to parse, if you come up with some kind of hygienic set of macros that fit within the language syntax, it might be very simple to implement micro-evaluation as parse tree manipulations. That’s just not an area that I’m particularly interested in.
Why did you choose to use strict formatting in source code?
Guido: The choice of indentation for grouping was not a novel concept in Python; I inherited this from ABC, but it also occurred in occam, an older language. I don’t know if the ABC authors got the idea from occam, or invented it independently, or if there was a common ancestor. The idea may be attributed to Don Knuth, who proposed this as early as 1974.
Of course, I could have chosen not to follow ABC’s lead, as I did in other areas (e.g., ABC used uppercase for language keywords and procedure names, an idea I did not copy), but I had come to like the feature quite a bit while using ABC, as it seemed to do away with a certain type of pointless debate common amongst C users at the time, about where to place the curly braces. I also was well aware that readable code uses indentation voluntarily anyway to indicate grouping, and I had come across subtle bugs in code where the indentation disagreed with the syntactic grouping using curly braces—the programmer and any reviewers had assumed that the indentation matched the grouping and therefore not noticed the bug. Again, a long debugging session taught a valuable lesson.
Strict formatting should produce a cleaner code and probably reduce the differences in the “layout” of the code of different programmers, but doesn’t this sound like forcing a human being to adapt to the machine, instead of the opposite path?
Guido: Quite the contrary—it helps the human reader more than it helps the machine; see the previous example. Probably the advantages of this approach are more visible when maintaining code written by another programmer.
New users are often put off by this initially, although I don’t hear about this so much any more; perhaps the people teaching Python have learned to anticipate this effect and counter it effectively.
I would like to ask you about multiple implementations of Python. There are four or five big implementations, including Stackless and PyPy.
Guido: Stackless, technically, is not a separate implementation. Stackless is often listed as a separate Python implementation because it is a fork of Python that replaces a pretty small part of the virtual machine with a different approach.
Basically the byte code dispatch, right?
Guido: Most of the byte code dispatch is very similar. I think the byte codes are the same and certainly all of the objects are the same. What they do different is when you have a call from one Python procedure to another procedure: they do that with manipulation of objects, where they just push a stack of stack frames and the same bit of C code remains in charge. The way it’s done in C Python is that, at that point, a C function is invoked which will then eventually invoke a new instance of the virtual machine. It’s not really the whole virtual machine, but the loop that interprets the byte code. There’s only one of those loops on the C stack in stackless. In traditional C Python, you can have that same loop on your C stack many times. That’s the only difference.
Start-up time hurt you there.
It sounds like you’re generally supportive of these implementations.
Guido: I have always been supportive of alternate implementations. From the day that Jim Hugunin walked in the door with a more or less completed JPython implementation, I was excited about it. In a sense, it counts as a validation of the language design. It also means that people can use their favorite language on the platform where otherwise they wouldn’t have access to it. We still have a way to go there, but it certainly helped me isolate which features were really features of the language that I cared about, and which features were features of a particular implementation where I was OK with other implementations doing things differently. That’s where we ended up on the unfortunately slippery slope of garbage collection.
That’s always a slippery slope.
Guido: But it’s also necessary. I cannot believe how long we managed to live with pure reference counting and no way to break cycles. I have always seen reference counting as a way of doing garbage collection, and not a particularly bad one. There used to be this holy war between reference counting versus garbage collection, and that always seemed rather silly to me.
Regarding these implementations again, I think Python is an interesting space because it has a pretty good specification. Certainly compared to other languages like Tcl, Ruby, and Perl 5. Was that something that came about because you wanted to standardize the language and its behavior, or because you were looking at multiple implementations, or something else?
Guido: It was probably more a side effect of the community process around PEPs and the multiple implementations. When I originally wrote the first set of documentation, I very enthusiastically started a language reference manual, which was supposed to be a sufficiently precise specification that someone from Mars or Jupiter could implement the language and get the semantics right. I never got anywhere near fulfilling that goal.
What we do have is enough understanding of how the language is supposed to work, and enough unit tests, and enough people on hand that can answer to implementers of other versions in finite time. I know that, for example, the IronPython folks have been very conscientious in trying to run the entire Python test suite, and for every failure deciding if the test suite was really testing the specific behavior of the C Python implementation or if they actually had more work to do in their implementation.
The PyPy folks did the same thing, and they went one step further. They have a couple of people who are much smarter than I, and who have come up with an edge case probably prompted by their own thinking about how to generate code and how to analyze code in a JIT environment. They have actually contributed quite a few tests and disambiguations and questions when they found out that there was a particular combination of things that nobody had ever really thought about. That was very helpful. The process of having multiple implementations of the language has been tremendously helpful for getting the specification of the language disambiguated.
Do you foresee a time when C Python may not be the primary implementation?
Guido: That’s hard to see. I mean some people foresee a time where .NET rules the world; other people foresee a time where JVMs rule the world. To me, that all seems like wishful thinking. At the same time, I don’t know what will happen. There could be a quantum jump where, even though the computers that we know don’t actually change, a different kind of platform suddenly becomes much more prevalent and the rules are different.
Perhaps a shift away from the von Neumann architecture?
Guido: I wasn’t even thinking of that, but that’s certainly also a possibility. I was more thinking of what if mobile phones become the ubiquitous computing device. Mobile phones are only a few years behind the curve of the power of regular laptops, which suggests that in a few years, mobile phones, apart from the puny keyboard and screen, will have enough computing power so that you don’t need a laptop anymore. It may well be that mobile phones for whatever platform politics end up all having a JVM or some other standard environment where C Python is not the best approach and some other Python implementation would work much better.
There’s certainly also the question of what do we do when we have 64 cores on a chip, even in a laptop or in a cell phone. I don’t actually know if that should change the programming paradigm all that much for most of the things we do. There may be a use for some languages that let you specify incredibly subtle concurrent processes, but in most cases the average programmer cannot write correct thread-safe code anyway. Assuming that somehow the ascent of multiple cores forces them to do that is kind of unrealistic. I expect that multiple cores will certainly be useful, but they will be used for coarse-grained parallelism, which is better anyway, because with the enormous cost difference between cache hits and cache misses, main memory no longer really serves the function of shared memory. You want to have your processes as isolated as possible.
How should we deal with concurrency? At what level should this problem be dealt with or, even better, solved?
Guido: My feeling is that writing single-threaded code is hard enough, and writing multithreaded code is way harder—so hard that most people don’t have a hope of getting it right, and that includes myself. Therefore, I don’t believe that fine-grained synchronization primitives and shared memory are the solution—instead, I’d much rather see messagepassing solutions get back in style. I’m pretty sure that changing all programming languages to add synchronization constructs is a bad idea.
I also still don’t believe that trying to remove the GIL from CPython will work. I do believe that some support for managing multiple processes (as opposed to threads) is a piece of the puzzle, and for that reason Python 2.6 and 3.0 will have a new standard library module, multiprocessing, that offers an API similar to that of the threading module for doing exactly that. As a bonus, it even supports processes running on different hosts!
Expedients and Experience
Is there any tool or feature that you feel is missing when writing software?
Guido: If I could sketch on a computer as easily as I can with pencil and paper, I might be making more sketches while doing the hard thinking about a design. I fear that I’ll have to wait until the mouse is universally replaced by a pen (or your finger) that lets you draw on the screen. Personally, I feel terribly handicapped when using any kind of computerized drawing tool, even if I’m pretty good with pencil and paper—perhaps I inherited it from my father, who was an architect and was always making rough sketches, so I was always sketching as a teenager.
At the other end of the scale, I suppose I may not even know what I’m missing for spelunking large codebases. Java programmers have IDEs now that provide quick answers to questions like “where are the callers of this method?” or “where is this variable assigned to?” For large Python programs, this would also be useful, but the necessary static analysis is harder because of Python’s dynamic nature.
How do you test and debug your code?
Guido: Whatever is expedient. I do a lot of testing when I write code, but the testing method varies per project. When writing your basic pure algorithmic code, unit tests are usually great, but when writing code that is highly interactive or interfaces to legacy APIs, I often end up doing a lot of manual testing, assisted by command-line history in the shell or page-reload in the browser. As an (extreme) example, you can’t very well write a unit test for a script whose sole purpose is to shut down the current machine; sure, you can mock out the part that actually does the shut down, but you still have to test that part, too, or else how do you know that your script actually works?
Testing something in different environments is also often hard to automate. Buildbot is great for large systems, but the overhead to set it up is significant, so for smaller systems often you just end up doing a lot of manual QA. I’ve gotten a pretty good intuition for doing QA, but unfortunately it’s hard to explain.
When should debugging be taught? And how?
Guido: Continuously. You are debugging your entire life. I just “debugged” a problem with my six-year-old son’s wooden train set where his trains kept getting derailed at a certain point on the track. Debugging is usually a matter of moving down an abstraction level or two, and helped by stopping to look carefully, thinking, and (sometimes) using the right tools.
I don’t think there is a single “right” way of debugging that can be taught at a specific point, even for a very specific target such as debugging program bugs. There is an incredibly large spectrum of possible causes for program bugs, including simple typos, “thinkos,” hidden limitations of underlying abstractions, and outright bugs in abstractions or their implementation. The right approach varies from case to case. Tools come into play mostly when the required analysis (“looking carefully”) is tedious and repetitive. I note that Python programmers often need few tools because the search space (the program being debugged) is so much smaller.
How do you resume programming?
Guido: This is actually an interesting question. I don’t recall ever looking consciously at how I do this, while I indeed deal with this all the time. Probably the tool I used most for this is version control: when I come back to a project I do a diff between my workspace and the repository, and that will tell me the state I’m in.
If I have a chance, I leave XXX markers in the unfinished code when I know I am about to be interrupted, telling me about specific subtasks. I sometimes also use something I picked up from Lambert Meertens some 25 years ago: leave a specific mark in the current source file at the place of the cursor. The mark I use is “HIRO,” in his honor. It is colloquial Dutch for “here” and selected for its unlikeliness to ever occur in finished code. 🙂
At Google we also have tools integrated with Perforce that help me in an even earlier stage: when I come in to work, I might execute a command that lists each of the unfinished projects in my workspace, so as to remind me which projects I was working on the previous day. I also keep a diary in which I occasionally record specific hard-to-remember strings (like shell commands or URLs) that help me perform specific tasks for the project at hand—for example, the full URL to a server stats page, or the shell command that rebuilds the components I’m working on.
What are your suggestions to design an interface or an API?
Guido: Another area where I haven’t spent a lot of conscious thought about the best process, even though I’ve designed tons of interfaces (or APIs). I wish I could just include a talk by Josh Bloch on the subject here; he talked about designing Java APIs, but most of what he said would apply to any language. There’s lots of basic advice like picking clear names (nouns for classes, verbs for methods), avoiding abbreviations, consistency in naming, providing a small set of simple methods that provide maximal flexibility when combined, and so on. He is big on keeping the argument lists short: two to three arguments is usually the maximum you can have without creating confusion about the order. The worst thing is having several consecutive arguments that all have the same type; an accidental swap can go unnoticed for a long time then.
I have a few personal pet peeves: first of all, and this is specific to dynamic languages, don’t make the return type of a method depend on the value of one of the arguments; otherwise it may be hard to understand what’s returned if you don’t know the relationship— maybe the type-determining argument is passed in from a variable whose content you can’t easily guess while reading the code.
Second, I dislike “flag” arguments that are intended to change the behavior of a method in some big way. With such APIs the flag is always a constant in actually observed parameter lists, and the call would be more readable if the API had separate methods: one for each flag value.
Another pet peeve is to avoid APIs that could create confusion about whether they return a new object or modify an object in place. This is the reason why in Python the list method sort( ) doesn’t return a value: this emphasizes that it modifies the list in place. As an alternative, there is the built-insorted( ) function, which returns a new, sorted list.
Should application programmers adopt the “less is more” philosophy? How should they simplify the user interface to provide a shorter learning path?
Guido: When it comes to graphical user interfaces, it seems there’s finally growing support for my “less is more” position. The Mozilla foundation has hired Aza Raskin, son of the late Jef Raskin (codesigner of the original Macintosh UI) as a UI designer. Firefox 3 has at least one example of a UI that offers a lot of power without requiring buttons, configuration, preferences or anything: the smart location bar watches what I type, compares it to things I’ve browsed to before, and makes useful suggestions. If I ignore the suggestions it will try to interpret what I type as a URL or, if that fails, as a Google query. Now that’s smart! And it replaces three or four pieces of functionality that would otherwise require separate buttons or menu items.
This reflects what Jef and Aza have been saying for so many years: the keyboard is such a powerful input device, let’s use it in novel ways instead of forcing users to do everything with the mouse, the slowest of all input devices. The beauty is that it doesn’t require new hardware, unlike Sci-Fi solutions proposed by others like virtual reality helmets or eye movement sensors, not to mention brainwave detectors.
What do the lessons about the invention, further development, and adoption of your language say to people developing computer systems today and in the forseeable future?
Guido: I have one or two small thoughts about this. I’m not the philosophical kind, so this is not the kind of question I like or to which I have a prepared response, but here’s one thing I realized early on that I did right with Python (and which Python’s predecessor, ABC, didn’t do, to its detriment). A system should be extensible by its users. Moreover, a large system should be extensible at two (or more) levels.
Since the first time I released Python to the general public, I got requests to modify the language to support certain kinds of use cases. My first response to such requests is always to suggest writing some Python code to cover their needs and put it in a module for their own use. This is the first level of extensibility—if the functionality is useful enough, it may end up in the standard library.
The second level of extensibility is to write an extension module in C (or in C++, or other languages). Extension modules can do certain things that are not feasible in pure Python (though the capabilities of pure Python have increased over the years). I would much rather add a C-level API so that extension modules can muck around in Python’s internal data structures, than change the language itself, since language changes are held to the highest possible standard of compatibility, quality, semantic clarity, etc. Also, “forks” in the language might happen when people “help themselves” by changing the language implementation in their own copy of the interpreter, which they may distribute to others as well. Such forks cause all sorts of problems, such as maintenance of the private changes as the core language also evolves, or merging multiple independently forked versions that other users might need to combine. Extension modules don’t have these problems; in practice most functionality needed by extensions is already available in the C API, so changes to the C API are rarely necessary in order to enable a particular extension.
Another thought is to accept that you don’t get everything right the first time. Early on during development, when you have a small number of early adopters as users, is the time to fix things drastically as soon as you notice a problem, never mind backward compatibility. A great anecdote I often like to quote, and which has been confirmed as truthful by someone who was there at the time, is that Stuart Feldman, the original author of “Make” in Unix v7, was asked to change the dependence of the Makefile syntax on hard tab characters. His response was something along the lines that he agreed tab was a problem, but that it was too late to fix since there were already a dozen or so users.
As the user base grows, you need to be more conservative, and at some point absolute backward compatibility is a necessity. There comes a point where you have accumulated so many misfeatures that this is no longer feasible. A good strategy to deal with this is what I’m doing with Python 3.0: announce a break with backward compatibility for one particular version, use the opportunity to fix as many such issues as possible, and give the user community a lot of time to deal with the transition.
In Python’s case, we’re planning to support Python 2.6 and 3.0 alongside each other for a long time—much longer than the usual support lifetime of older releases. We’re also offering several transitional strategies: an automated source-to-source conversion tool that is far from perfect, combined with optional warnings in version 2.6 about the use of functionality that will change in 3.0 (especially if the conversion tool cannot properly recognize the situation), as well as selective back-porting of certain 3.0 features to 2.6. At the same time, we’re not making 3.0 a total rewrite or a total redesign (unlike Perl 6 or, in the Python world, Zope 3), thereby minimizing the risk of accidentally dropping essential functionality.
One trend I’ve noticed in the past four or five years is much greater corporate adoption of dynamic languages. First PHP, Ruby in some context, definitely Python in other contexts, especially Google. That’s interesting to me. I wonder where these people were 20 years ago when languages like Tcl and Perl, and Python a little bit later, were doing all of these useful things. Have you seen desire to make these languages more enterprise-friendly, whatever that means?
Guido: Enterprise-friendly is usually when the really smart people lose interest and the people of more mediocre skills have to somehow fend for themselves. I don’t know if Python is harder to use for mediocre people. In a sense you would think that there is quite a bit of damage you cannot do in Python because it’s all interpreted. On the other hand, if you write something really huge and you don’t use enough unit testing, you may have no idea what it actually does.
You’ve made the argument that a line of Python, a line of Ruby, a line of Perl, a line of PHP, may be 10 lines of Java code.
Guido: Often it is. I think that the adoption level in the enterprise world, even though there are certain packages of functionality that are helpful, is probably just a fear of very conservative managers. Imagine the people in charge of IT resources for 100,000 people in a company where IT is not a main product—maybe they are building cars, or doing insurance, or something else, but everything they do is touched by computers. The people in charge of that infrastructure necessarily have to be very conservative. They will go with stuff that looks like it has a big name attached, like maybe Sun or Microsoft, because they know that Sun and Microsoft screw up all the time, but these companies are obliged to recover from those screwups and fix them, even if it takes five years.
Open source projects traditionally have just not offered that same peace of mind to the average CIO. I don’t know exactly if and how and when that will change. It’s possible that if Microsoft or Sun suddenly supported Python on their respective VMs, programmers in enterprises would actually discover that they can get higher productivity without any downsides by using more advanced languages.