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Speaker 1

00:00

The following is a conversation with Brian Kernighan, a professor of computer science at Princeton University. He was a key figure in the computer science community in the early Unix days, alongside Unix creators Ken Thompson and Dennis Ritchie. He co-authored the C programming language with Dennis Ritchie, the creator of C, and has written a lot of books on programming, computers, and life, including the practice of programming, the Go programming language, and his latest, Unix, a history, and a Memoir. He co-created AWK, the text processing language used by Linux folks like myself.

Speaker 1

00:39

He co-designed Ample, an algebraic modeling language that I personally love and have used a lot in my life for large scale optimization. I think I can keep going for a long time with his creations and accomplishments, which is funny because given all that, he's 1 of the most humble and kind people I've spoken to on this podcast. Quick summary of the ads. 2 new sponsors, the amazing, self-cooling 8 Sleep mattress and Raycon earbuds.

Speaker 1

01:13

Please consider supporting the podcast by going to eightsleep.com slash Lex and going to buyraycon.com slash Lex. Click the links, buy the stuff. It really is the best way to support this podcast and the journey I'm on. If you enjoy this thing, subscribe on YouTube, review it with Firestarz and Apple Podcast, support it on Patreon, or connect with me on Twitter at Lex Friedman.

Speaker 1

01:39

As usual, I'll do a few minutes of ads now, and never any ads in the middle that can break the flow of the conversation. This show is sponsored by 8Sleep and its incredible Pod Pro mattress that you can check out at 8sleep.com slash Lex to get $200 off. The mattress controls temperature with an app and can cool down to as low as 55 degrees. Research shows that temperature has a big impact on the quality of our sleep.

Speaker 1

02:08

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Speaker 1

02:26

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Speaker 1

02:51

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Speaker 1

03:10

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Speaker 1

03:30

In fact, for fun, I have 1 of the earbuds in now and I'm listening to Europa by Santana, probably 1 of my favorite guitar songs. It kind of makes me feel like I'm in a music video. So, they told me to say that a bunch of celebrities use these, like Snoop Dogg, Melissa Etheridge, and Cardi B. I don't even know who Cardi B is, but her earbud game is on point.

Speaker 1

03:55

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Speaker 1

04:17

It really helps this podcast. And now, here's my conversation with Brian Kernighan.

Speaker 2

04:25

Unix started being developed 50 years ago, maybe more than 50 years ago. Can you tell the story, like you describe in your new book, of how Unix was created?

Speaker 3

04:35

Ha, if I can remember that far back, it was some while ago. So I think the gist of it is that at Bell Labs In 1969, there were a group of people who had just finished working on the Multics project, which was itself a follow-on to CTSS. So we can go back sort of an infinite regress in time, but the CTSS was a very, very, very nice time-sharing system.

Speaker 3

05:00

It was very nice to use. I actually used it as that summer I spent in Cambridge in 1966.

Speaker 2

05:06

What was the hardware there? So what's the operating system? What's the hardware there?

Speaker 2

05:10

What's the CTS look like?

Speaker 3

05:12

So CTSS looked like kind of like a standard time-sharing system. Certainly at the time, it was the only time-sharing.

Speaker 2

05:19

Let's go back to the basic. What's the time-sharing system?

Speaker 3

05:22

Okay. In the beginning was the word and the word was the sound. And then

Speaker 2

05:25

there was time-sharing systems. Yeah.

Speaker 3

05:27

If we go back into, let's call it the 1950s and early 1960s, most computing was done on very big computers, physically big, although not terribly powerful by today's standards, that were maintained in very large rooms. And you use things like punch cards to write your programs on and talk to them. So you would take a deck of cards, write your program on it, send it over a counter, hand it to an operator.

Speaker 3

05:53

And some while later back would come something that said, Oh, you made a mistake. And then you'd recycle. And so it was very, very slow. So the idea of time-sharing was that you take basically that same computer, but connect to it with something that looked like an electric typewriter.

Speaker 3

06:09

That could be a long distance away, it could be close, but fundamentally what the operating system did was to give each person who was connected to it and wanting to do something a small slice of time to do a particular job. So I might be editing a file, so I would be typing. And every time I hit a keystroke, the operating system would wake up and said, oh, he typed character, let me remember that. And then he'd go back to doing something else.

Speaker 3

06:34

So it'd be going around and around a group of people who were trying to get something done, giving each a small slice of time and giving them each the illusion that they pretty much had the whole machine to themselves. And hence, time sharing, that is sharing the computing time resource of the computer among a number of people who were doing it.

Speaker 2

06:54

Without the individual people being aware that there's others, in a sense, the illusion, the feelings that the machine is your own.

Speaker 3

07:02

Pretty much that was the idea. Yes, you had, if it were well done, and if it were fast enough and other people weren't doing too much, you did have the illusion that you had the whole machine to yourself. And it was very much better than the punch card model.

Speaker 3

07:16

And so CTSS, the compatible time-sharing system, was I think arguably the first of these. It was done, I guess, technically in 64, something like that. It ran on an IBM 7094, slightly modified to have twice as much memory as the norm. It had 2 banks of 32K words instead of 1.

Speaker 3

07:37

So, it's just you

Speaker 2

07:38

were- 32K words, yeah.

Speaker 3

07:40

Each word was 36 bits, so call it, you know, about 150 kilobytes times 2. So, by today's standards, that's down in the noise. But at the time, that was a lot of memory and memory was expensive.

Speaker 3

07:53

So CTSS was just a wonderful environment to work on. It was done by the people at MIT, led by Fernando Corbato, Corby, who died just earlier this year, and a bunch of other folks. So I spent the summer of 66 working on that. Had a great time, met a lot of really nice people, and indirectly knew of people at Bell Labs who were also working on a follow-on to CTSS that was called Multics.

Speaker 3

08:24

So Multics was meant to be the system that would do everything that CTSS did, but do it better for a larger population. All the usual stuff.

Speaker 2

08:31

Now the actual time sharing, the scheduling, how much, what's the algorithm that performs the scheduling? What's that look like? How much magic is there?

Speaker 2

08:40

What are the metrics? How does it all work in the beginning?

Speaker 3

08:44

So The answer is I don't have a clue. I think the basic idea was nothing more than who all wants to get something done. Suppose that things are very quiet in the middle of the night, then I get all the time that I want.

Speaker 3

08:55

Suppose that you and I are contending at high noon for something like that, then probably the simplest algorithm is a round robin 1 that gives you a bit of time, gives me a bit of time. And then we could adapt to that. Like, what are you trying to do? Are you text editing or are you compiling or something?

Speaker 3

09:11

And then we might adjust the scheduler according to things like that.

Speaker 2

09:15

So, okay, so Multics was trying to just do some of the, clean it up a little bit.

Speaker 3

09:20

Well, it was meant to be much more than that. So Multics was the multiplexed information and computing service, and it was meant to be a very large thing that would provide computing utility, something that where you could actually think of it as just a plug in the wall service, sort of like cloud computing today, same idea, but 50 odd years earlier. And so what Multics offered was a richer operating system environment, a piece of hardware that was better designed for doing the kind of sharing of resources, and presumably lots of other things.

Speaker 2

09:55

Do you think people at that time had the dream of what cloud computing is starting to become now, which is computing is everywhere that you can just plug in almost, you know, and you never know how the magic works. You just kind of plug in, add your little computation that you need to perform and it does it. Was that the dream?

Speaker 3

10:14

I don't know where that was the dream. I wasn't part of it at that point. Remember, I was an intern for a summer.

Speaker 3

10:19

But my sense is, given that it was over 50 years ago, yeah, they had that idea that it was an information utility, that it was something where if you had a computing task to do, you could just go and do it. Now I'm betting that they didn't have the same view of computing for the masses, let's call it, the idea that your grandmother would be shopping on Amazon. I don't think that was part of it. But if your grandmother were a programmer, it might be very easy for her to go and use this kind of utility.

Speaker 2

10:51

What was your dream of computers at that time? What did you see as the future of computers? Could you have predicted what computers are today?

Speaker 3

10:59

Oh, short answer, absolutely not. I have no clue. I'm not sure I had a dream.

Speaker 3

11:03

It was a dream job in the sense that I really enjoyed what I was doing. I was surrounded by really, really nice people. Cambridge is a very fine city to live in in the summer, less so in the winter when it snows, but in the summer It was a delightful time. And so I really enjoyed all of that stuff and I learned things.

Speaker 3

11:20

And I think the good fortune of being there for summer led me then to get a summer job at Bell Labs the following summer. And that was quite useful for the future.

Speaker 2

11:31

So this Bell Labs is this magical, legendary place. So first of all, where is Bell Labs? And can you start talking about that journey towards Unix at Bell Labs?

Speaker 3

11:46

Yeah, so Bell Labs is physically scattered around, at the time, scattered around New Jersey. The primary location was in a town called Murray Hill, or a location called Murray Hill. It was actually across the boundary between 2 small towns in New Jersey called New Providence and Berkeley Heights.

Speaker 3

12:03

Think of it as about 15, 20 miles straight west of New York City, and therefore about an hour north of here in Princeton. And at that time, it had make up a number, 3,000 or 4,000 people there, many of whom had PhDs and mostly doing physical sciences, chemistry, physics, materials, kinds of things, but very strong math and rapidly growing interest in computing as people realized you could do things with computers that you might not have been able to do before. You could replace labs with computers that had worked on models of what was going on. So that was the essence of Bell Labs.

Speaker 3

12:44

And again, I wasn't a permanent employee there. That was another internship. I got lucky in internships.

Speaker 2

12:50

I mean, if you could just linger on it a little bit, what was in the air there? Because some of the, the number of Nobel Prizes, the number of Turing Awards, and just legendary computer scientists that come from there, inventions including, developments including Unix, it's just, it's unbelievable. So was there something special about that place?

Speaker 3

13:11

Oh, I think there was very definitely something special. I mentioned the number of people, it's a very large number of people, very highly skilled and working in an environment where there was always something interesting to work on because the goal of Bell Labs, which was a small part of AT&T, which provided basically the country's phone service, The goal of AT&T was to provide service for everybody. And the goal of Bell Labs was to try and make that service keep getting better.

Speaker 3

13:37

So improving service. And that meant doing research on a lot of different things. Physical devices like the transistor or fiber, optical cables or microwave systems, all of these things the labs worked on. And it was kind of just the beginning of real boom times in computing as well.

Speaker 3

13:57

Because when I was there, I went there first in 66. So computing was at that point fairly young and so people were discovering that you could do lots of things with computers.

Speaker 2

14:08

So how's Unix born? So Multics,

Speaker 3

14:12

in spite of having an enormous number of really good ideas and lots of good people working on it fundamentally didn't live up, at least in the short run, and I think ultimately really ever, to its goal of being this information utility. It was too expensive and certainly what was promised was delivered much too late. And so in roughly the beginning of 1969, Bell Labs pulled out of the project.

Speaker 3

14:37

The project at that point had included MIT, Bell Labs, and General Electric. General Electric made computers, So General Electric was the hardware operation. So Bell Labs, realizing this wasn't going anywhere on a timescale they cared about, pulled out a project. And this left several people with an acquired taste for really, really nice computing environments, but no computing environment.

Speaker 3

15:03

And so they started thinking about what could you do if you're going to design a new operating system that would provide the same kind of comfortable computing as CTSS had, but also the facilities of something like Multics sort of brought forward. So they did a lot of paper design stuff. And at the same time, Ken Thompson found what is characterized as a little used PDP-7, where he started to do experiments with file systems, just how do you store information on a computer in an efficient way. And then this famous story that his wife went away to California for 3 weeks taking their one-year-old son, and 3 weeks, and he sat down and wrote an operating system, which ultimately became Unix.

Speaker 3

15:47

So software productivity was good in those days.

Speaker 2

15:50

So PDP, what's a PDP-7? So it's a piece of hardware.

Speaker 3

15:53

Yeah, it's a piece of hardware. It was 1 of early machines made by Digital Equipment Corporation, DEC, And it was a mini computer, so-called. It had, I would have to look up the numbers exactly, but it had a very small amount of memory, maybe 16K, 16-bit words or something like that, relatively slow, probably not super expensive.

Speaker 3

16:17

Maybe, again, making this up, I'd have to look it up, $100,000 or something like that.

Speaker 2

16:21

Which is not super expensive in those days, right?

Speaker 3

16:24

It was expensive, it was enough that you and I probably wouldn't be able to buy 1, but a modest group of people could get together. But in any case, it came out, if I recall, in 1964. So by 1969, it was getting a little obsolete, and that's why it was little used.

Speaker 2

16:41

If you can sort of comment, what do you think it's like to write an operating system like that? So that process that Ken went through in 3 weeks, because you were, I mean, you're a part of that process. You contributed a lot to Unix's early development.

Speaker 2

16:57

So what do you think it takes to do that first step, that first kind of, from design to reality on the PDP?

Speaker 3

17:05

Well, let me correct 1 thing. I had nothing to do with it. So I did not write it.

Speaker 3

17:10

I have never written operating system code. And so I don't know. Now an operating system is simply code. And this first 1 wasn't very big, but it's something that lets you run processes of some, lets you execute some kind of code that has been written.

Speaker 3

17:27

It lets you store information for periods of time so that it doesn't go away when you turn the power off or reboot or something like that. And there's a kind of a core set of tools that are technically not part of an operating system, but you probably need them. In this case, Ken wrote an assembler for the PDP-7 that worked. He needed a text editor so that he could actually create text.

Speaker 3

17:49

He had the file system stuff that he had been working on and then the rest of it was just a way to load things, executable code from the file system into the memory, give it control and then recover control when it was finished or in some other way quit.

Speaker 2

18:04

What was the code written in the primarily the programming language, was it in assembly?

Speaker 3

18:09

Yeah, PDP 7 assembler that Ken created. These things were assembly language until probably the, call it 1973 or 74, something like that.

Speaker 2

18:21

Forgive me if it's a dumb question, but it feels like a daunting task to write any kind of complex system in assembly.

Speaker 3

18:28

Absolutely.

Speaker 2

18:31

It feels like impossible to do any kind of what we think of as software engineering with assembly because to work in a big picture sort of. I think it's hard.

Speaker 3

18:41

It's been a long time since I wrote assembly language. It is absolutely true that in assembly language, if you make a mistake, nobody tells you. There are no training wheels whatsoever.

Speaker 3

18:49

And so stuff doesn't work. Now what?

Speaker 2

18:51

And you're- There's no debuggers.

Speaker 3

18:53

Well, there could be debuggers, but that's the same problem, right? How do you actually get something that will help you debug it? So part of it is an ability to see the big picture.

Speaker 3

19:05

Now, these systems were not big in the sense that today's pictures are. So the big picture was in some sense more manageable. I mean, then realistically, there's an enormous variation in the capabilities of programmers. And Ken Thompson, who did that first 1, is kind of the singularity, in my experience, of programmers, with no disrespect to you or even to me.

Speaker 3

19:28

He's in several leagues removed.

Speaker 2

19:30

I know there's levels. It's a fascinating thing that there are unique stars in particular in the programming space and at a particular time. You know, the time matters too, the timing of when that person comes along.

Speaker 2

19:44

And a wife does have to leave. There's this weird timing that happens that and then all of a sudden something beautiful is created. I mean, how does it make you feel that there's a system that was created in 3 weeks or maybe you can even say on a whim, but not really, but of course quickly, that is now, you could think of most of the computers in the world run on a Unix-like system. Right.

Speaker 2

20:12

How do you, like, if you kind of zoom from the alien perspective, if you were just observing Earth, that all of a sudden these computers took over the world and they started from this little initial seed of Unix. How does that make you feel?

Speaker 3

20:26

It's quite surprising, and you asked earlier about prediction. The answer is no. There's no way you could predict that kind of evolution.

Speaker 3

20:34

And I don't know whether it was inevitable or just a whole sequence of blind luck. I suspect more of the latter. And so I look at it and think, gee, that's kind of neat. I think the real question is, what does Ken think about that?

Speaker 3

20:50

Because he's the guy arguably from whom it really came. Tremendous contributions from Dennis Ritchie and then others around in that Bell Labs environment. But if you had to pick a single person, that would be Ken.

Speaker 2

21:04

So you've written a new book, Unix, a history and a memoir. Are there some memorable human stories, funny or profound from that time that just kind of stand out?

Speaker 3

21:14

Oh, there's a lot of them in a sense. And again, it's a question of can you resurrect them in real time? If memory fails.

Speaker 3

21:21

But I think part of it was that Bell Labs at the time was a very special kind of place to work because there were a lot of interesting people and the environment was very, very open and free. It was a very cooperative environment, very friendly environment. And so if you had an interesting problem, you go and talk to somebody and they might help you with the solution. And it was a kind of a fun environment too in which people did strange things and often tweaking the bureaucracy in 1 way or another.

Speaker 2

21:52

So rebellious in certain kinds of ways.

Speaker 3

21:54

In some ways, yeah, absolutely. I think most people didn't take too kindly to the bureaucracy and I'm sure the bureaucracy put up with an enormous amount that they didn't really want to.

Speaker 2

22:05

So maybe to linger on it a little bit, do you have a sense of what the philosophy that characterizes Unix is, the design, not just the initial, but just carry through the years, just being there, being around it. What's the fundamental philosophy behind the system?

Speaker 3

22:23

I think 1 aspect of the fundamental philosophy was to provide an environment that made it easy to write or easier, productive to write programs. So it was meant as a programmer environment. It wasn't meant specifically as something to do some other kind of job.

Speaker 3

22:38

For example, it was used extensively for word processing, but it wasn't designed as a word processing system. It was used extensively for lab control, but it wasn't designed for that. It was used extensively as a front end for big other systems, big dump systems, but it wasn't designed for that. It was meant to be an environment where it was really easy to write programs.

Speaker 3

22:58

So the programmers could be highly productive. And part of that was to be a community. And there's some observation from Dennis Ritchie, I think at the end of the book, that says that from his standpoint, the real goal was to create a community where people could work as programmers on a system. And I think in that sense, certainly for many, many years, it succeeded quite well at that.

Speaker 3

23:22

And part of that is the technical aspects of, because it made it really easy to write programs, people did write interesting programs, those programs tended to be used by other programmers. And so it was kind of a virtuous circle of more and more stuff coming out that was really good for programmers.

Speaker 2

23:39

And you were part of that community of programmers. So what was it like writing programs on that early Unix?

Speaker 3

23:45

It was a blast, it really was. You know, I like to program. I'm not a terribly good programmer, but it was a lot of fun to write code.

Speaker 3

23:55

And in the early days, there was an enormous amount of what you would today, I suppose, call low-hanging fruit. People hadn't done things before, and this was this new environment, and the whole combination of nice tools and very responsive system and tremendous colleagues made it possible to write code. You could have an idea In the morning, you could do an experiment with it. You could have something limping along that night or the next day, and people would react to it, and they would say, oh, that's wonderful, but you really screwed up here.

Speaker 3

24:28

And the feedback loop was then very, very short and tight. And so a lot of things got developed fairly quickly that in many cases still exist today. And I think that was part of what made it fun because programming itself is fun. It's puzzle solving in a variety of ways, but I think it's even more fun when you do something that somebody else then uses Even if they whine about it not working the fact that they used it is is part of the reward mechanism

Speaker 2

24:58

And what was the method of an interaction the communication when you do that feedback loop? I mean this is before the Internet

Speaker 3

25:05

certainly before the Internet It was mostly physical right there, somebody would come into your office and say something.

Speaker 2

25:13

So these places are all close by, like offices are nearby, so you're really lively in interaction.

Speaker 3

25:18

Yeah, yeah, no, Bell Labs was fundamentally 1 giant building, and most of the people were involved in this Unix stuff were in 2 or 3 quarters, and there was a room, oh, how big was it? Probably, call it 50 feet by 50 feet, make up a number of that, which had some access to computers there, as well as in offices and people hung out there and it had a coffee machine. And so it was mostly very physical.

Speaker 3

25:46

We did use email, of course, but it was fundamentally for a long time all on 1 machine, so there was no need for internet.

Speaker 2

25:56

It's fascinating to think about what computing would be today without Bell Labs. It seems so many people being in the vicinity of each other, sort of getting that quick feedback, working together, so many brilliant people. I don't know where else that could have existed in the world and being given how that came together.

Speaker 2

26:17

Yeah, how does that make you feel that that's that little element of history?

Speaker 3

26:23

Well, I think that's very nice, but in a sense it's survivor bias. And if it hadn't happened at Bell Labs, there were other places that were doing really interesting work as well. Xerox PARC is perhaps the most obvious 1.

Speaker 3

26:34

Xerox PARC contributed an enormous amount of good material. And many of the things we take for granted today in the same way came from Xerox PARC experience. I don't think they capitalized in the long run as much. Their parent company was perhaps not as lucky in capitalizing on this, who knows?

Speaker 3

26:52

But that's certainly another place where there was a tremendous amount of influence. There were a lot of good university activities. MIT was obviously no slouch in this kind of thing, and others as well.

Speaker 2

27:07

So Unix turned out to be open source because of the various ways that AT&T operated and sort of it had to, It was, the focus was on telephones. So, well.

Speaker 3

27:18

I think that's a mischaracterization in a sense. It absolutely was not open source. It was very definitely proprietary, licensed, but it was licensed freely to universities in source code form for many years.

Speaker 3

27:33

And because of that, generations of university students and their faculty people grew up knowing about Unix. And there was enough expertise in the community that it then became possible for people to go off in their own direction and build something that looked Unix-like. The Berkeley version of Unix started with that licensed code and gradually picked up enough of its own code contributions, notably from people like Bill Joy, that eventually it was able to become completely free of any AT&T code. Now there was an enormous amount of legal jockeying around this in the late, early to late 80s, early 90s, something like that.

Speaker 3

28:19

And then, I guess the open source movement might have started when Richard Stallman started to think about this in the late 80s. And by 1991, when Torvalds decided he was going to do a Unix-like operating system, there was enough expertise in the community that first he had a target. He could see what to do because the Unix system call interface and the tools and so on were there. And so he was able to build an operating system that at this point, when you say Unix, in many cases what you're really thinking is Linux.

Speaker 2

28:58

Linux, yeah. But It's funny that from my distant perception, I felt that Unix was open source without actually knowing it, but what you're really saying, it was just freely licensed.

Speaker 3

29:11

It was freely licensed.

Speaker 2

29:12

So it felt open source, because universities are not trying to make money, so it felt open source in a sense that you can get access if you wanted.

Speaker 3

29:20

Right, and a very, very, very large number of universities had the license and they were able to talk to all the other universities who had the license. And so technically not open, technically belonging to AT&T, pragmatically pretty open.

Speaker 2

29:34

And so there's a ripple effect that all the faculty and the students then all grew up and then they went throughout the world and permeated in that kind of way. So what kind of features do you think make for a good operating system? If you take the lessons of Unix, you said, you know, make it easy for programmers.

Speaker 2

29:58

Like that seems to be an important 1. But also Unix turned out to be exceptionally robust and efficient. So is that an accident when you focus on the programmer or is that a natural outcome?

Speaker 3

30:14

I think Part of the reason for efficiency was that it began on extremely modest hardware. Very, very, very tiny. And so you couldn't get carried away.

Speaker 3

30:23

You couldn't do a lot of complicated things because you just didn't have the resources, either processor speed or memory. And so that enforced a certain minimality of mechanisms and maybe a search for generalizations so that you would find 1 mechanism that served for a lot of different things rather than having lots of different special cases. I think the file system in Unix is a good example of that. The file system interface in its fundamental form is extremely straightforward.

Speaker 3

30:53

And that means that you can write code very, very effectively for the file system. And then 1 of those generalizations is that, gee, that file system interface works for all kinds of other things as well. And so in particular, the idea of reading and writing to devices is the same as reading and writing to a disk that has a file system. And then that gets carried further in other parts of the world.

Speaker 3

31:18

Processes become, in effect, files in a file system. And the Plan 9 operating system, which came along, I guess, in the late 80s or something like that, took a lot of those ideas from the original Unix and tried to push the generalization even further so that in plan 9, a lot of different resources are file systems. They all share that interface. So that would be 1 example where finding the right model of how to do something means that an awful lot of things become simpler and it means therefore that more people can do useful, interesting things with them without having to think as hard about it.

Speaker 2

31:54

So you said you're not a very good programmer. That's true. You're the world's most Modest human being, okay, but you'll continue saying that.

Speaker 2

32:03

I understand how this works. But you do radiate a sort of love for programming. So let me ask, do you think programming is more an art or a science? Is it creativity or kind of rigor?

Speaker 3

32:16

I think it's some of each. It's some combination. Some of the art is figuring out what it is that you really want to do, what should that program be, what would make a good program, and that's some understanding of what the task is, what the people who might use this program want.

Speaker 3

32:33

I think that's art in many respects. The science part is trying to figure out how to do it well. Some of that is real computer science-y stuff, like what algorithm should we use at some point? Mostly in the sense of being careful to use algorithms that will actually work properly, scale properly, avoiding quadratic algorithms when a linear algorithm should be the right thing, that kind of more formal view of it.

Speaker 3

33:03

Same thing for data structures. But also it's, I think, an engineering field as well. And engineering is not quite the same as science because engineering, you're working with constraints. You have to figure out not only what is a good algorithm for this kind of thing, but what's the most appropriate algorithm given the amount of time we have to compute, the amount of time we have to program, what's likely to happen in the future with maintenance, who's going to pick this up in the future, all of those kind of things that if you're an engineer, you get to worry about.

Speaker 3

33:37

Whereas if you think of yourself as a scientist, well, you can maybe push them over the horizon in a way. And if you're an artist, what's that?

Speaker 2

33:45

So just on your own personal level, what's your process like of writing a program? Say a small and large sort of tinkering with stuff. Do you just start coding right away and just kind of evolve iteratively with a loose notion or do you plan on a sheet of paper first and then kind of design what they teach you in the kind of software engineering courses in undergrad or something like that?

Speaker 2

34:13

What's your process like?

Speaker 3

34:15

It's certainly much more the informal incremental. First, I don't write big programs at this point. It's been a long time since I wrote a program that was more than, I call it a few hundred or more lines, something like that.

Speaker 3

34:26

Many of the programs I write are experiments for either something I'm curious about or often for something that I wanna talk about in a class. And so those necessarily tend to be relatively small. A lot of the kind of code I write these days tends to be for sort of exploratory data analysis where I've got some collection of data and I wanna try and figure out what on earth is going on in it. And for that, those programs tend to be very small.

Speaker 3

34:52

Sometimes you're not even programming, you're just using existing tools like counting things. Or sometimes you're writing ox scripts because 2 or 3 lines will tell you something about a piece of data. And then when it gets bigger, well, then I will probably write something in Python because that scales better. Up to call it a few hundred lines or something like that.

Speaker 3

35:14

And it's been a long time since I wrote programs that were much more than that.

Speaker 2

35:18

Speaking of data exploration and awk, first, what is awk?

Speaker 3

35:23

So awk is a scripting language that was done by myself, L. Aho, and Peter Weinberger. We did that originally in the late 70s.

Speaker 3

35:32

It was a language that was meant to make it really easy to do quick and dirty tasks like counting things or selecting interesting information from basically all text files, rearranging it in some way or summarizing it.

Speaker 2

35:48

It runs a command on each line of a file. I mean, it's still exceptionally widely used today.

Speaker 3

35:55

Oh, absolutely, yeah.

Speaker 2

35:56

And it's so simple and elegant, sort of the way to explore data. Turns out you can just write a script that does something seemingly trivial on a single line, and that giving you that slice of the data somehow reveals something fundamental about the data. And that keeps, that seems to work still.

Speaker 3

36:16

Yeah, it's very good for that kind of thing. And that's sort of what it was meant for. I think what we didn't appreciate was that the model was actually quite good for a lot of data processing kinds of tasks and that it's kept going as long as it has because at this point it's over 40 years old, and it's still, I think, a useful tool.

Speaker 3

36:36

And well, this is paternal interest, I guess, but I think in terms of programming languages, you get the most bang for the buck by learning awk. And it doesn't scale to big programs, but it does pretty darn well on these little things where you just want to see all the somethings in something. So yeah, I find I probably write more awk than anything else at this point.

Speaker 2

36:58

So what kind of stuff do you love about awk? Like is there, if you can comment on sort of things that give you joy when you can, in a simple program, reveal something about the data? Is there something that stands out from particular features?

Speaker 3

37:15

I think it's mostly the selection of default behaviors. You sort of hinted at it a moment ago. What Ock does is to read through a set of files and then within each file, it reads through each of the lines.

Speaker 3

37:28

And then on each of the lines, It has a set of patterns that it looks for. That's your awk program. And if 1 of the patterns matches, there is a corresponding action that you might perform. And so it's kind of a quadruply nested loop or something like that.

Speaker 3

37:45

And that's all completely automatic. You don't have to say anything about it. You just write the pattern and the action and then run the data by it. And so that paradigm for programming is very natural and effective 1.

Speaker 3

37:56

And I think we captured that reasonably well in Ock. And it does other things for free as well. It splits the data into fields so that on each line there's fields separated by white space or something. And so it does that for free.

Speaker 3

38:08

You don't have to say anything about it. And it collects information as it goes along. Like what line are we on? How many fields are there on this line.

Speaker 3

38:17

So lots of things that just make it so that a program which in another language, let's say Python, would be 5, 10, 20 lines in Arc is 1 or 2 lines.

Speaker 2

38:27

And so because it's 1 or 2 lines, you can do it on the shell. Like you don't have to open up another whole thing. You can just do it right there in the interaction with the operatives directly.

Speaker 2

38:38

Is there other shell commands that you love over the years like you really enjoy using? Oh, grep. Grep?

Speaker 3

38:47

Grep's the only 1. Yeah, Grep does everything.

Speaker 2

38:51

So Grep is a kind of a, what is it? A simpler version of awk, I would say?

Speaker 3

38:55

In some sense, yeah, right, because- What is Grep? So Grep is, it basically searches the input for particular patterns, regular expressions, technically of a certain class. And it has that same paradigm that Ock does.