Writing bugs with K.S. Bhaskar

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Hello and welcome to Fork Around and Find Out, the podcast all about taking you back in
time on what it's like to write bugs for the last five decades.

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Today on the show we have Bhaskar, who we met in person like many of the guests, which is
great.

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I met you at dinner and you said something that stuck with me still to this day, where you
said you've been writing the finest bugs since the 1970s.

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And I love that.

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So thank you for coming on the show.

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Thank you for having me.

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It's good to be here.

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And of course, the software business, we create bugs, right?

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That's what we do.

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That's it, no features, just bugs.

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And sometimes they're usable bugs.

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Exactly.

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wish that we could just say that quote to computer science classes because I think it
would help a lot of imposter syndrome.

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uh

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you you will write this wrong five times and then you will read it, right?

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But it'll still be wrong in the future.

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So don't worry It's not it's either a bug right now or it's a bug later

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The amount of engineers, I think especially women that I talked to and they're just like,
is it normal to feel bad so much?

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And I'm like, yeah.

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There's a lot of failing like, and then you feel like a God for like two seconds and then
there's a lot of failing.

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Yeah.

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When the build works and you're like, understand that piece of it.

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And then the next thing is just like, I don't get it.

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Anything.

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Nothing works.

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think that should be part of teaching engineering though, like we're almost doing them a
disservice when we're just like, and then it'll build, like, cause I mean, like that

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happens a lot less than the failing part.

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So if like we were more, I guess, transparent on that cycle, I think that people could
find more joy in being an engineer, you know?

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In the old days we used to say if it compiled, ship it.

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Yeah.

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Yeah, if it compiled, that's the test right there.

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If I have something that executes, right, I that's good enough.

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Which is wild with the amount of testing and pipelines that we use now, you know,
someone's dying when they're hearing that.

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Oh man, like a flaky test.

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You're like, oh, we can't just stop everyone stop.

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Cause this doesn't work all the time.

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And one of the things, one of the things I want to talk to you about is just how software
has changed over a period of time that you've, you've been able to experience and, and how

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you've kind of changed with it, right?

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Like the industry, even in last 10 years is different 20 years ago, 30 years ago, it
completely not even recognizable.

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Well, here's something too, which is when I got my first job programming.

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I was a student at my undergraduate university in India.

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And I got my first job as a programmer in 1971 part-time, and I was paid two rupees an
hour.

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Now, at that time, the biggest computer that we had on campus was an IBM 7044, which today
would be dwarfed computationally by a smart doorbell.

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You

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computer time on that sold for 2000 rupees an hour.

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Okay, so a thousand times more than I was paid.

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And so the lesson always was, what would a professor has told us is if you can spend two
hours debugging or fixing something to save a couple seconds of computer time, it's worth

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it.

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And, know, today it's completely the other way around.

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Yeah, that's probably the biggest change, Because now somewhat one person's time is worth
so much more than the compute.

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Right.

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You know, and I think nothing of, you know, I make a change.

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I just run a compile to see if I got the syntax right.

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And, you know, those days I wouldn't even think of doing that because one of the things
you had to do was, you know, we had punch cards and you wrote your, your program on punch

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cards.

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You turned it in and you would basically get a printout the next day.

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And so if you didn't get it right, then you had to wait yet another day for the program to
run.

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Has that made us worse as engineers?

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Like, I feel like because we don't value like the computer, like we just, we're just like,
just either throw money at it or try it again or do little things over and over again.

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Is that, is that as an engineering culture?

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Well, it's good and bad.

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mean, there are things where, you know, certainly I'm a lot more productive now.

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And I think that the code I write today is probably a lot better than the code I wrote 50
years ago just because, you know, I can test it better, I can run more cycles on it and so

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on.

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On the other hand, you know, there are certain things where you have to, you know, let's
say if you're doing something that is really critical.

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it really does pay to stop and think about it and think through it before you just write
the code.

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And certainly in the business that YaraDB is in, we go into a lot of mission-critical
applications in banking and healthcare.

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And so it's absolutely critical to us to get the code right.

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of course, so it works one way in the sense that we can do a lot more testing now than
we've.

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could have done years ago.

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But on the other hand, it also means we have to spend a lot more time thinking about the
code and thinking about the design even before we start coding.

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And in the old days, we didn't think about it that much because, again, programs are also
simpler.

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Sure.

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Was that simply, was the simplification because of they were not used as in many places or
was that just because they were isolated and not as dependent on other pieces of software

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and things?

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Well, you literally couldn't create a big program.

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So like the IBM 7044 computer, its memory was something like 32K words, and each word was
36 bits.

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So if it didn't fit in memory, you essentially had to sort of overlay parts of the
program, bring in bits and pieces.

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So what you actually did was write your program in lots of little things, and you went
from.

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You process something and then put it on tape and then you process something else.

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So you couldn't do anything that is particularly big.

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And I guess in some sense what comes around goes around because that's what microservices
is.

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Except in our case, we did one service at a time instead of running them all in parallel.

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Maybe microservices would be better if we counted how much memory they used in words.

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Or is that you get 50 words of memory.

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Just a...

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Think about the fact that we were counting words and that kind of processing and then like
the huge data sets we're processing right now for like machine learning.

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those two, like the fact that they're in a span of your career is absolutely amazing.

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It's been a lot of fun.

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What I tell people is, you know, I'm 70 and I got into computers.

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So, okay, so here's the reason I got into computers.

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The computer center was the only air-conditioned building on campus.

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that that is why okay but like have you ever heard anyone that's come on this show that's
really done great things everybody's got like the most unique reason for starting and I

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love that like nobody's just because like

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I I got into computers because the computer lab was across the hall from my dorm room and
I could do my homework while I got paid.

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And it was just like, could get paid and do homework.

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And that's the perfect thing as a college student.

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And I just sit there.

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But air conditioning, that is the best example.

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on the campus there, routinely in summer, the temperature would get to over 100 degrees.

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And again, this is India, and it was less developed in those days than it is today.

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So we would often have power cuts in the dorm from like 5 o'clock in the morning till 11
o'clock in the morning.

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So no fan even.

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So the computer center has got electricity.

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It's got air conditioning.

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It's a great place to go hang out.

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How did the university get a computer at that time?

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I did not grow up around computers.

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I was born in the 80s.

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everything, it seems like that seems like a really big privilege for that university to
have that big of a computer and air conditioning and constant power at that time.

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So this was the Indian Institute of Technology in Kanpur.

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And it was set up as part of with aid from a consortium of nine American universities,
know, MIT, Ohio State, Purdue, I forget there was a list of nine.

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And they, somewhere in like the mid 1960s, they had a spare IBM 1620 computer.

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Now the IBM 1620, by the way,

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goes back, it's a 1950s design.

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It was IBM's first scientific computer.

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So anyway, they had one to spare and they shipped it and installed it on campus.

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So that was the computer that I used to write my first program.

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And at that time in India, there were probably about, if I remember correctly, somewhere
around 120 or 130 computers.

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And my university had three, so we were quite privileged.

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But it was...

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One of those things where the government of India set up these institutes to sort of be
like the next, to train the next generation of engineers.

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so ours was set up with American collaboration.

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There was one that was set up with British collaboration.

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Another one, you know, German, another one Soviet.

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And then there was one that was set up with UNESCO collaboration.

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And so essentially in each of these universities, the country as part of their foreign aid
program would provide expertise and...

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and equipment and so on to set up the university.

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Were there a lot of differences between the contrast of the different countries, I guess,
and their...

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uh

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think the big difference was what they specialized in, what are some of the prestige
courses.

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So in my university, electrical engineering was considered the prestige course.

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In the Indian Institute of Technology in Bombay, which is set up with Soviet
collaboration, chemical engineering was a prestige course.

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In the one that was set up with German collaboration, for example, mechanical engineering
was considered a prestige course.

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All the universities had all the different branches of engineering and we had a very
competitive exam to get in.

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they would like, know, an entrance exam there was something like 40,000 people would sit
for the exam and out of those maybe a couple thousand would get in.

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And depending on your rank in the exam, you got to select which course at which university
you got into.

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Wow, you must have been, you've done really well.

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I did pretty well.

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So I actually, you know, I was the only one I remember in that year that came in the first
hundred in both the entrance exam for the Indian Institute of Technology as well as there

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was another thing called the National Science Talent Search, which is kind of modeled on
what used to be the Westinghouse Science Talent and then is the Intel Science Talent.

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So I actually...

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That particular year, I came first in the country on that.

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So was the only one that managed to come in in the first hundred on both.

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Did you have previous experience in electrical engineering before that?

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No, no, actually it was a long story, it's a short story actually.

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My passion in high school was physics, that's what I wanted to do in college.

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And so when I came first in the science talent, that's really what I wanted to do.

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And then my dad said, well, you're going to do electrical engineering and you're going to
do electrical engineering at this university.

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And I argued with him for several days, but ultimately I had no choice.

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man.

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then you still pick the building that had AC.

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I picked the building that had AC and again, at that time there was no undergraduate
computer science program, so electrical engineering was the closest you could get to

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computer science.

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And in fact, that is a time when I actually had the opportunity to sort of unite both.

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So Stanford University shipped us an old PDP-1 computer that they had worked on and they
had heavily modified.

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So they shipped it to us when...

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They didn't have any further use for it.

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And a bunch of us spent like a year putting it together.

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It filled a whole room.

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And Stanford had modified it.

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So here's something for debugging software.

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One of the instructions that Stanford had added to it was called fiddle following.

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So if you executed a fiddle following instruction, the next instruction could be something
completely different from what was one of the official instructions of the instruction

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set.

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So it was a lot of fun.

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what kind of, maybe the other people know what this is, but what did it entail to take a
whole day to put together, or that whole time to put together a computer?

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Well, mean, we actually had some, you when it was shipped to us, it wasn't really like
Lego where you had to put it together.

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The computer was kind of created and put together.

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But what it did mean was that if the program didn't work, you had to go look at the
hardware, not just the software.

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So a flip-flop was essentially a circuit board about this size.

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It used germanium PNP transistors and

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And if the flip-flop circuit was loose, your program might not run properly.

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wow, that makes debugging way more complicated.

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Like I feel like I should be more grateful for the things I have to check at this point.

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It just goes into like...

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how much software engineers were also hardware engineers, right?

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You had to know how the hardware worked.

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Autumn, I finished the book you bought me for Christmas, The Superman, which was a story
of Seymour Cray.

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And a lot of the things that he talked about, they deciding on, they want silicone
transistors or germanium transistors?

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It was like a big deal for them to decide because everything else was germanium at the
time.

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Like, no, we're gonna go with silicon, right?

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And it's just fascinating how those ripples have kind of affected so many things.

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And I remember one of the big problems they kept having in the the Cray computers that
they're building was just the heat dissipation.

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They're like, well, we were hand soldering all these transistors.

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These were not integrated circuits.

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These were big pieces of equipment.

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And they had to have like AC specialists on hand to be able to like, how do we cool this
much heat in this much electricity?

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Awesome.

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And this also points out to me just, think you are living proof of investment in other
countries and being able to allow, not allow people to, but just like giving people access

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to things they may not have access to before.

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like your career is like proof that that works and that that is something that is
continued even today that should be invested in long term for all people, not just to like

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say, you know, only people that live in this area can have access things.

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And you didn't get access to the top tier hardware at the time, like you were getting
secondhand computers, but it still like allowed you to do so much more than what Stanford

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was gonna do with that PDP one anymore, right?

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Sure, absolutely.

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And in fact, I remember back in I forget, 1980s and 1990s, one of the things that we were
doing was getting used computers and then shipping them to places like Central America and

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Africa and so on, where one of those things would actually be very useful.

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And I recently ran across a kid that was getting old laptops and sending them to India.

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I would argue not only is it an investment because look at how great it is that you got a
career, I mean, look at how much you've given back to the industry.

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You know what I mean?

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So it's not like they just gave things to you.

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You've given back so much, which shows that it is such a great...

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People just look at kind of helping others or kind of making that democratization of tech
as like, oh, well, we're helping people, but they help us back.

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We have had so many technological breakthroughs, science breakthroughs.

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that add to everyone.

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Like this is a team sport of like discoveries.

208
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We do so much more when we work together.

209
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Oh, absolutely.

210
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And I think that helping other people be productive helps us be productive as well.

211
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And certainly living in the US, it's easy to forget that.

212
00:16:55,074 --> 00:17:05,558
And certainly living in the US under the, I don't know what your particular political
leanings are, but certainly living in the US at the present, where we tend to say, let's

213
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not worry about people outside.

214
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I think it's a mistake because ultimately,

215
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You're absolutely right.

216
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We do live together and we sort of build on each other.

217
00:17:16,191 --> 00:17:25,576
I was actually thinking that this is such a great conversation for the time that we're in
because how many cancer researches, how many, just all kinds of research were saying that,

218
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you know, all these wonderful students got here on merit.

219
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They got here because they were amazing at their field.

220
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And we knew that it was this great investment because they were going to give 10 times
more than what they've put in and that we were going to make these breakthroughs.

221
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And it just makes me so sad that we are going to lose so much advancement because we
can't.

222
00:17:45,855 --> 00:17:50,280
look at this in a community kind of way and we're looking at it in a selfish way.

223
00:17:51,705 --> 00:17:58,958
desire to save a dollar is just ridiculous to think that we don't make that money back.

224
00:17:58,958 --> 00:18:00,601
uh Yeah.

225
00:18:00,601 --> 00:18:01,665
ah

226
00:18:01,665 --> 00:18:11,211
like taxes when you're like not a citizen, it's actually we're going to spend more money
to get rid of people that we really need that make things better.

227
00:18:12,142 --> 00:18:14,856
Sure, I absolutely agree.

228
00:18:15,282 --> 00:18:22,082
So in 71, you started writing bugs and then you caught the bug for programming.

229
00:18:22,602 --> 00:18:24,462
How did that go on?

230
00:18:24,462 --> 00:18:28,722
What was it like for you, your career in, let's say, 80s and 90s?

231
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What was the next kind of wave that you were doing?

232
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a nutshell.

233
00:18:32,322 --> 00:18:34,502
So actually I started programming in 1970.

234
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1971 was when I got my first job.

235
00:18:37,762 --> 00:18:42,422
so I came to the US as a grad student in 1975.

236
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And that was like this great big shift in technology because I was using these old
computers at college in India.

237
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And I came to Carnegie Mellon Computer Science Department in the US.

238
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And there we had deck tens and then

239
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using them, using a terminal interactively, that was something that was completely new to
me.

240
00:19:05,133 --> 00:19:11,956
And then actually I went on to the University of Nebraska where we had like a personal APL
machine.

241
00:19:11,956 --> 00:19:15,217
There's a programming language called APL.

242
00:19:15,217 --> 00:19:23,280
we actually had a machine that ran APL and that was my first computer that I saw that was
a standalone desktop computer.

243
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Yeah, so we moved on to that.

244
00:19:25,111 --> 00:19:27,630
And then I went to work for uh

245
00:19:27,630 --> 00:19:33,530
a company in Seattle called Fluke for many years, and we built electronic test equipment.

246
00:19:33,770 --> 00:19:40,490
And I actually spent a good chunk of my time working on test equipment in particular.

247
00:19:40,490 --> 00:19:48,470
For example, there's test equipment for the Boeing used in the 757 and 767 to test the
radio altimeter.

248
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So I led the software on that.

249
00:19:51,110 --> 00:19:53,590
And it's a time of significant change.

250
00:19:53,590 --> 00:19:54,630
we actually

251
00:19:54,926 --> 00:20:00,986
retargeted a compiler that we actually did the first piece of test equipment that was
written in a high-level language.

252
00:20:00,986 --> 00:20:04,146
Until then, people had done it all in assembly language.

253
00:20:04,406 --> 00:20:11,586
So we actually took a C compiler, and then we retargeted that from the 8080 to the Z80,
and then wrote that.

254
00:20:11,626 --> 00:20:18,586
And it was kind of interesting because we had 48 kilobytes of RAM and 16 kilobytes of RAM.

255
00:20:18,586 --> 00:20:24,009
And so we'd write the software, and then we'd say, oh crap, it's like six bytes bigger
than 48 kilobytes.

256
00:20:24,009 --> 00:20:24,654
jeez.

257
00:20:24,654 --> 00:20:30,941
So then we would sit at it and we wrote a people optimize, know, and then we do people
optimization.

258
00:20:30,941 --> 00:20:35,606
We'd say, okay, here's the sequence of code which can be shrunk by this little bit.

259
00:20:35,606 --> 00:20:44,024
So we'd add that to the people optimizer and then it would run a compilation and then we'd
say, wow, okay, now we have 10 bytes to play with.

260
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it is different.

261
00:20:46,857 --> 00:20:48,038
oh

262
00:20:48,056 --> 00:20:51,402
at that point you're trying to out-optimize the compiler.

263
00:20:52,322 --> 00:21:02,425
We were trying to, well, basically as part of the compiler, we did a people optimization
phase at the end of the compilation.

264
00:21:02,565 --> 00:21:03,255
so did that.

265
00:21:03,255 --> 00:21:10,207
And then ultimately at some point, and I went on to work for other hardware companies that
were doing software.

266
00:21:10,207 --> 00:21:19,970
And then I realized back in the 1980s that I really needed to work for a software company
because one of the problems of working for a hardware company, it's an accounting problem.

267
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Companies are slaves.

268
00:21:21,272 --> 00:21:22,922
to their accounting systems.

269
00:21:22,923 --> 00:21:36,288
So if you think about it, a hardware company like Fluke, they would burden their labor
costs based on, because they had to have all these massive equipment for building

270
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equipment.

271
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And so let's say the labor costs they would charge is like, let's pick a number, oh $75 an
hour.

272
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Now, if you think about software in those days, you wrote the software, but then the
manufacturing cost of the software.

273
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is just you duplicate a floppy disk, you print it manually, shrink wrap it in a box, and
you ship it.

274
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So maybe it's 15 minutes of time.

275
00:21:58,709 --> 00:22:07,106
But if you're charging your labor at $75 enough, and there's 15 minutes of time that does
not require heavy equipment, all of a sudden, you're going to conclude that software is

276
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not a profitable business to be in.

277
00:22:10,699 --> 00:22:19,798
And so I decided to move into the software world, went to work for a company that did
electronic.

278
00:22:19,798 --> 00:22:21,249
test and measurement software.

279
00:22:21,249 --> 00:22:29,714
In fact, that company had the first fast Fourier transform that ran on a PC and then moved
into databases.

280
00:22:29,714 --> 00:22:41,120
They've been working in databases for the last 30 years and that's what Garadibi is, we're
a database company and you're basically making very high-end key value databases.

281
00:22:41,330 --> 00:22:47,116
So what was that switch like going from like a lot of testing software to database?

282
00:22:47,116 --> 00:22:57,328
was in, I mean, databases weren't new, but I do feel like databases have gone through a
lot of changes over the last 20 to 30 years.

283
00:22:58,316 --> 00:23:10,916
Well, in our case, we've actually stayed with the key value technology just because
there's a lot of software that was written back in the 1980s and 1990s.

284
00:23:11,036 --> 00:23:12,877
I we still use that today.

285
00:23:13,318 --> 00:23:22,365
One of the things that's fashionable these days is to say that this software is old, so
therefore it must be bad.

286
00:23:23,426 --> 00:23:25,127
And that doesn't make any sense.

287
00:23:25,127 --> 00:23:28,270
I the analogy I use is to like bicycles, right?

288
00:23:28,270 --> 00:23:34,630
If you look at a bicycle, for one of my presentations, I have a photo of a bicycle from
the 1920s.

289
00:23:34,630 --> 00:23:36,510
And I have a photo of a bicycle from today.

290
00:23:36,510 --> 00:23:42,330
And it's one of those things where if you were to see that bicycle from the 1920s, you
would have no problem riding it.

291
00:23:42,550 --> 00:23:49,410
And conversely, the guy from the 1920s were to see the bicycle today, maybe he'd have to
understand how gears work, but he'd have no problem riding a bike.

292
00:23:49,810 --> 00:23:52,730
And so, old isn't necessarily bad.

293
00:23:53,730 --> 00:23:58,374
And so one of the things that, at least in the database business that I've been in,

294
00:23:58,968 --> 00:24:03,018
We run a lot of legacy applications.

295
00:24:03,018 --> 00:24:10,801
of that code, parts of it may have been written in the 1980s, and maybe today it's 100
times bigger than what it was in the 1980s.

296
00:24:10,801 --> 00:24:16,563
But the code is something that is living, it has grown, it has a lot more functionality.

297
00:24:16,883 --> 00:24:22,805
And we continue to insist that the code that is written in the 1980s continues to run
today.

298
00:24:22,805 --> 00:24:27,778
So it's very different from the type of database business that you...

299
00:24:27,778 --> 00:24:32,702
you tend to see outside where, you here's something new, let's pick it up.

300
00:24:35,058 --> 00:24:46,567
I would say databases is usually a little more conservative in wanting to try the newest
greatest thing because the closer you get to critical data, the more important it is that

301
00:24:46,567 --> 00:24:50,047
that code is tested well and kind of battle tested in the real world.

302
00:24:50,047 --> 00:24:51,715
It's also a pain to migrate.

303
00:24:51,715 --> 00:24:55,037
Like migrations are so painful and they take so long.

304
00:24:56,984 --> 00:25:01,895
Well, and among other things, you have to make sure that you migrated correctly, right?

305
00:25:01,895 --> 00:25:09,797
If you have terabytes of data, you can't just say, know, CP from this file to this file,
and suddenly you've got the database moved over.

306
00:25:10,318 --> 00:25:20,560
So, and you know, we run, and especially because we're running in banking and healthcare,
we have to also be very conscious about security.

307
00:25:21,201 --> 00:25:27,052
So it's not just that it has to be right, because your bank balance is just a few bits on
a disk.

308
00:25:27,542 --> 00:25:31,731
it also has to have other safeguards.

309
00:25:32,708 --> 00:25:44,087
So how do you, like maintaining databases and maintaining code bases for decades, like how
do you take a different mindset to how you're going to maintain something that long,

310
00:25:44,087 --> 00:25:44,322
right?

311
00:25:44,322 --> 00:25:52,273
Because there's so many things today that's like, maintain some open source project for
maybe six months, a year, maybe a few years, but something like, hey, if we want to

312
00:25:52,273 --> 00:25:58,285
maintain this for 30 years, what kind of things do you need set up upfront?

313
00:25:58,285 --> 00:26:00,244
how do you maintain it through all the changes?

314
00:26:00,244 --> 00:26:03,656
Because the world's changed so much, you know?

315
00:26:04,878 --> 00:26:06,878
Well, certainly the...

316
00:26:06,878 --> 00:26:08,538
Well, one is, of course, evolution.

317
00:26:08,538 --> 00:26:13,398
mean, it's not like suddenly things change and something is broken.

318
00:26:13,638 --> 00:26:17,358
But the, you know, the code base has evolved.

319
00:26:17,738 --> 00:26:21,678
It originally, you know, if you go back 30, 40...

320
00:26:21,678 --> 00:26:26,118
The code base actually is almost 40 years old when it was first written.

321
00:26:26,118 --> 00:26:31,938
And it ran on a Motorola 68000 VMS computer system.

322
00:26:32,288 --> 00:26:34,990
And then it was migrated to a VAX and then to an alpha.

323
00:26:34,990 --> 00:26:39,063
And then it was migrated to UNIX.

324
00:26:39,063 --> 00:26:43,286
And then like 25 years ago, we migrated that to Linux.

325
00:26:43,286 --> 00:26:48,609
And so with each of these migrations, there's certainly evolution that comes along.

326
00:26:48,630 --> 00:26:55,584
But part of what you have to do when you maintain a code base with that law is, well, let
me take a step back.

327
00:26:56,320 --> 00:26:58,191
and talk about software testing, right?

328
00:26:58,191 --> 00:27:03,253
So if you want to maintain code for that long, you've got to have a lot of testing that
goes with it.

329
00:27:03,313 --> 00:27:08,955
So the goal of testing is not just to prove that the software does what it's supposed to
do.

330
00:27:09,175 --> 00:27:14,958
You also have to prove that the software, you have to have confidence that the software
doesn't do what it's not supposed to do.

331
00:27:16,178 --> 00:27:20,900
And then the question is, how do you have that confidence that the software is not doing
what it's not supposed to do?

332
00:27:20,900 --> 00:27:25,222
Because you can't possibly test for all of those things.

333
00:27:25,304 --> 00:27:34,236
So the way that you do that in practice is you test that the software does everything that
it's supposed to do, as well as a few diabolical cases that you throw at it.

334
00:27:34,737 --> 00:27:46,560
And so even if you make a small change somewhere, you still have to go through the entire
test cycle to convince yourself that you haven't broken something somewhere unrelated.

335
00:27:47,440 --> 00:27:49,541
So there's a certain mindset.

336
00:27:49,541 --> 00:27:52,361
There's a certain way of writing software.

337
00:27:52,382 --> 00:27:54,142
You tend to stick to

338
00:27:55,170 --> 00:28:02,775
Let's say non-APIs, you don't necessarily go chase the newest shiny thing that comes
along.

339
00:28:03,696 --> 00:28:08,459
So those are all the ways that we keep the oh lasting for a long time.

340
00:28:08,459 --> 00:28:14,523
And I think that if it's properly written, it should still be running 100 years from now.

341
00:28:14,523 --> 00:28:19,006
I won't be around 100 years from now, but the code should still be around and still be
running.

342
00:28:19,447 --> 00:28:26,527
One of my favorite parts of talking to you so far is people won't be able to see it
because of podcasts, but you have just the kindest face.

343
00:28:26,527 --> 00:28:30,827
And when you talk about the technology, you still look excited.

344
00:28:30,827 --> 00:28:33,387
And I love that because people will be like, well, what do you want to do when you retire?

345
00:28:33,387 --> 00:28:34,627
And I was like, I hope I never retire.

346
00:28:34,627 --> 00:28:42,467
I don't want to work nine to five forever, but like, I hope I get to kind of always play
with technology and.

347
00:28:43,647 --> 00:28:44,224
Yes.

348
00:28:44,224 --> 00:28:45,806
things and from enjoying it.

349
00:28:45,806 --> 00:28:46,737
Yeah.

350
00:28:46,918 --> 00:28:47,758
Yeah.

351
00:28:47,853 --> 00:28:48,573
and enjoyment.

352
00:28:48,573 --> 00:28:53,186
How have you, I guess, pivoted?

353
00:28:53,186 --> 00:28:57,088
I've watched your talks long ago just because being in the database world.

354
00:28:57,088 --> 00:29:02,472
And the way that you speak of technology just makes me excited about it.

355
00:29:02,472 --> 00:29:04,323
How have you kept that excitement?

356
00:29:04,323 --> 00:29:06,344
Because you do great things.

357
00:29:06,344 --> 00:29:07,875
You advocate for open source.

358
00:29:07,875 --> 00:29:10,707
You advocate for access to like in...

359
00:29:10,707 --> 00:29:11,727
uh

360
00:29:12,137 --> 00:29:19,568
Allowing people into IT technology like you've done a lot of so many amazing things in
your career and you still have that same excitement Like you just started yesterday.

361
00:29:19,568 --> 00:29:21,110
How do you keep that going?

362
00:29:22,338 --> 00:29:26,800
Well, there are two reasons to be in business, make money and have fun.

363
00:29:26,800 --> 00:29:36,854
you know, you have to, with the make money part, you you need enough to keep a roof over
your head and put bread on the table, but I'm not out to, you know, buy a Caribbean island

364
00:29:36,854 --> 00:29:38,204
or something like that.

365
00:29:38,344 --> 00:29:43,726
And, but the fun part is, you know, I found something that I enjoy doing.

366
00:29:44,787 --> 00:29:49,529
And once I found something that I enjoyed doing, I kind of stuck with it.

367
00:29:49,789 --> 00:29:51,990
And what I tell people is that,

368
00:29:53,102 --> 00:30:00,449
I would like to keep writing software until it's time for me to be carried out
horizontally because that's what I enjoy doing.

369
00:30:00,449 --> 00:30:06,034
And that's my bucket list and I'm able to do that.

370
00:30:06,034 --> 00:30:10,528
So it's not right for everyone obviously, but it's right for me.

371
00:30:10,753 --> 00:30:22,355
They say that if you, oh, they have that saying like, if you do what you enjoy, you never
work a day in your life and your face just like, I hope to have a career that's half as

372
00:30:22,355 --> 00:30:24,427
cool as yours where I still look that excited.

373
00:30:24,427 --> 00:30:28,602
Like what is it that you love so much about databases and open source?

374
00:30:28,602 --> 00:30:33,685
Because like you are just such a component for Linux databases and open source.

375
00:30:33,685 --> 00:30:36,659
And I love the quote where you always say that open source is good for business.

376
00:30:36,659 --> 00:30:39,912
And I think we're kind of in a weird spot in open source right now.

377
00:30:39,912 --> 00:30:47,591
So like, I would love to hear kind of like, what keeps bringing you back to that and like
what joy you find in databases and open source in Linux.

378
00:30:48,866 --> 00:30:54,729
Well, databases just happens to be something that I stumbled into when I was looking for a
job.

379
00:30:54,729 --> 00:30:56,080
And then it got me interested.

380
00:30:56,080 --> 00:30:58,391
And once I got interested, I kind of stayed interested with it.

381
00:30:58,391 --> 00:31:09,658
But what I like about open source is that it transfers power from the hands of the
developer to the hands of the user.

382
00:31:09,798 --> 00:31:15,721
And that was brought home to me when I was running a small business in Massachusetts many
years ago.

383
00:31:17,036 --> 00:31:24,070
bug tracking in our software, had this software that cost like $1,000 or $2,000 or
something like that.

384
00:31:24,070 --> 00:31:27,211
I forget the amount, but it was something that a small company could afford.

385
00:31:27,472 --> 00:31:32,705
And then that company got bought by another company that got bought by a bigger company.

386
00:31:32,705 --> 00:31:41,619
And suddenly this $2,000 piece of software became an enterprise software with like a
quarter of a million dollar entry price.

387
00:31:43,041 --> 00:31:46,062
And about that time, I was also

388
00:31:46,706 --> 00:31:53,261
I started using Emacs, I was influenced by Richard Stallman and some of the work that he
was doing.

389
00:31:53,261 --> 00:32:03,460
And I realized that you really need to shift the balance of power from the developer to
the user.

390
00:32:03,460 --> 00:32:11,506
And the way to do that is open source or free software, free as in Libre, not free as in
Beard, though the two go together.

391
00:32:11,547 --> 00:32:16,290
And so that's how I sort of became a convert to using open source.

392
00:32:16,290 --> 00:32:31,393
Now the transition to doing open source as a business came later and that was mostly, I
was working for this company, our software was proprietary and then we got bought by this

393
00:32:31,393 --> 00:32:35,086
other company because they wanted the technology, they were our biggest customer.

394
00:32:35,587 --> 00:32:41,772
And they didn't want to market that software, they just said we'll continue supporting our
old customers.

395
00:32:41,852 --> 00:32:44,394
But then what I could see was that over time,

396
00:32:44,598 --> 00:32:46,698
And this goes back to like 2000.

397
00:32:46,919 --> 00:32:59,222
Over time, what would happen is that as the bar for quality kept getting higher and
higher, if the number of users stayed the same or shrank, then eventually what is going to

398
00:32:59,222 --> 00:33:06,024
happen was that you're going to be spending all of your time on quality and on testing and
less of the time on the software itself.

399
00:33:06,084 --> 00:33:08,424
So we had to expand the user base.

400
00:33:09,225 --> 00:33:14,378
And the parent company was kind of skeptical about open source, but I said, hey, let's go.

401
00:33:14,378 --> 00:33:18,579
open source, let's release the software, we'll get a lot more users.

402
00:33:19,320 --> 00:33:29,524
And so they went along with that and surprise, surprise, all of a sudden, we got a lot
more users and some of those users turned into customers because they were running these

403
00:33:29,524 --> 00:33:32,485
critical applications and needed someone behind them.

404
00:33:32,986 --> 00:33:38,668
And that was when I became a fan of open source as a business.

405
00:33:39,588 --> 00:33:44,150
And frankly, what we do is we sell peace of mind.

406
00:33:44,302 --> 00:33:53,722
So if someone is using our software and they're using it because their business depends on
it, they need someone behind them and by having us behind them, they have peace of mind

407
00:33:53,722 --> 00:33:55,082
and that's what we're sailing.

408
00:33:55,935 --> 00:33:56,399
I wish.

409
00:33:56,399 --> 00:33:57,100
a lot of these are, right?

410
00:33:57,100 --> 00:34:07,245
Especially open source where the lower in the stack and the more critical the software is,
the more people need to be able to sleep at night knowing that database is gonna have my

411
00:34:07,245 --> 00:34:09,158
data tomorrow, right?

412
00:34:09,158 --> 00:34:09,809
exactly.

413
00:34:09,809 --> 00:34:21,204
people would like remember what you just said about open source because I think that
everybody is in this rush to change licenses on people and they forget the reciprocal and

414
00:34:21,204 --> 00:34:28,607
like relationship people that there is with people being your customer and contributing to
your code base and using your software.

415
00:34:29,784 --> 00:34:39,146
Sure, and in fact just using software has value because if you use the software and you
report issues, that has value to a software developer.

416
00:34:42,694 --> 00:34:44,975
Yeah.

417
00:34:45,716 --> 00:34:55,971
You said you were influenced by Richard Stallman and I just recently read the Cathedral in
the Bazaar about open source uh software as opposed to the Free Software Foundation and

418
00:34:55,971 --> 00:34:57,321
what Richard Stallman was doing.

419
00:34:57,321 --> 00:35:00,473
How did you see that play out in what businesses were thinking?

420
00:35:00,473 --> 00:35:05,145
Because when I think of open source software and kind of the boom, I think of...

421
00:35:07,194 --> 00:35:16,047
not Mozilla, you know, like the browsers, the browser wars in the nineties where there was
these open source options of like there was internet explorer and Microsoft, and then

422
00:35:16,047 --> 00:35:18,098
there was the open source version.

423
00:35:18,098 --> 00:35:22,300
And that was really how like people saw open source could be a business.

424
00:35:22,300 --> 00:35:24,861
Cause there was this thing that was challenging the big monopoly.

425
00:35:24,861 --> 00:35:25,841
How is that?

426
00:35:25,841 --> 00:35:28,302
How have you seen that change over the years?

427
00:35:29,560 --> 00:35:37,151
Well, actually, interesting that you mentioned the Cathedral of the Bazaar because ESR
just lives a few miles down the road from where we are.

428
00:35:37,151 --> 00:35:38,577
if you know him, him come on the show.

429
00:35:38,577 --> 00:35:40,039
I'd love to talk to him.

430
00:35:40,039 --> 00:35:42,000
Well, I sort of know him.

431
00:35:42,000 --> 00:35:46,904
I gave him a ride once to the Southeast Linux Festival many years ago.

432
00:35:46,904 --> 00:35:51,007
oh Sure, I'll mention it to him.

433
00:35:51,048 --> 00:35:53,369
send him an email.

434
00:35:53,830 --> 00:36:08,066
No, I think the big cultural change, if you remember Steve Ballmer many years ago saying
Linux is a cancer, and then many years later, you

435
00:36:08,066 --> 00:36:10,487
Satya Nadella says, we love Linux.

436
00:36:11,148 --> 00:36:22,316
So I think that really summarizes the cultural shift that has happened where people saw
open source as a threat.

437
00:36:22,316 --> 00:36:24,518
Now they see that as an opportunity.

438
00:36:24,518 --> 00:36:36,736
And the sad part is now they see taking the software, you have these open core licenses
and openish licenses which aren't really open source, but which source available licenses.

439
00:36:37,048 --> 00:36:39,711
So that seems to be going in the opposite direction.

440
00:36:39,711 --> 00:36:45,707
But I can see why people want to do that because certainly making money in the open source
business is hard.

441
00:36:46,688 --> 00:36:48,670
But then making money in any business is hard.

442
00:36:48,670 --> 00:36:52,414
If I were running a restaurant, think making money in the restaurant business would be
hard.

443
00:36:52,725 --> 00:36:53,676
Very true.

444
00:36:53,676 --> 00:36:56,497
I just want to say that talking to you is a joy.

445
00:36:56,497 --> 00:37:00,249
you just, your energy just like totally makes me like so excited.

446
00:37:00,249 --> 00:37:04,591
What got, what started you with your love for Linux?

447
00:37:04,591 --> 00:37:10,143
Like what made you, what drove, what attracted you to Linux and what's kept you there for
so long?

448
00:37:11,352 --> 00:37:14,214
So it's open source Unix.

449
00:37:14,214 --> 00:37:16,455
So I've used Unix for many years.

450
00:37:16,455 --> 00:37:19,867
In fact, my first personal computer was a Unix computer.

451
00:37:19,867 --> 00:37:29,873
It was an AT &T 3B1 that had like a 40 megabyte disk and two megabytes of RAM or something
like that.

452
00:37:29,873 --> 00:37:31,494
And it ran Unix.

453
00:37:32,435 --> 00:37:38,222
And then when I got to Linux, the first Linux I got was actually a Linux that

454
00:37:38,222 --> 00:37:43,622
the entire Linux was on a floppy and you booted off the floppy and you ran it and it gave
you a shell.

455
00:37:44,022 --> 00:37:48,062
So it is like, cool, here's this Unix system.

456
00:37:48,262 --> 00:37:54,402
I have access to all the source code, I can play with it and that kind of got me into
Linux in the first place.

457
00:37:54,582 --> 00:38:00,358
And so I kicked the Windows habit probably around 1999 and...

458
00:38:00,547 --> 00:38:01,512
You missed XP.

459
00:38:01,512 --> 00:38:02,834
That was a good wave.

460
00:38:04,194 --> 00:38:09,483
will actually bought a used laptop once it had XP but then I installed Linux on it.

461
00:38:09,483 --> 00:38:11,913
that's good.

462
00:38:14,805 --> 00:38:24,399
What do you, if you had to pick one part of your career, what was like, like what was the
most exciting, I guess the highlight or what would you take away if you had to tell like

463
00:38:24,399 --> 00:38:28,213
your younger self about like this career that you could not have imagined?

464
00:38:28,415 --> 00:38:29,245
Like.

465
00:38:31,424 --> 00:38:33,085
Oh, that's hard to say.

466
00:38:33,085 --> 00:38:36,626
think that I've enjoyed every bit of it.

467
00:38:36,626 --> 00:38:47,400
I've actually changed jobs very few times, only three or four times in my entire career
because I've enjoyed doing what I do.

468
00:38:47,880 --> 00:38:54,102
So what I would tell my younger self is just to keep doing what you enjoy.

469
00:38:54,647 --> 00:38:56,751
Do you have any, oh, sorry.

470
00:38:57,358 --> 00:39:02,006
to what you said about being in business, it's you're making money or you're having fun.

471
00:39:02,006 --> 00:39:06,687
And I feel like in a lot of ways that second piece on having fun is not what you're doing.

472
00:39:06,687 --> 00:39:08,729
Like you're making fun too, right?

473
00:39:08,729 --> 00:39:11,100
Like you are creating the fun you want to have.

474
00:39:11,100 --> 00:39:14,814
You are making money and you are making fun to be able to enjoy this, right?

475
00:39:14,814 --> 00:39:19,699
Cause we can make work into a lot of different things and we can say, oh, this sucks.

476
00:39:19,699 --> 00:39:21,090
I hate everything.

477
00:39:21,212 --> 00:39:27,890
But if you kind of go into it with excitement and wanting to learn the things and wanting
to push yourself, you get to make your own fun.

478
00:39:27,890 --> 00:39:28,781
And that's really cool.

479
00:39:28,781 --> 00:39:30,984
That's what my kids do all day, every day, right?

480
00:39:30,984 --> 00:39:31,845
They get to play with their friends.

481
00:39:31,845 --> 00:39:34,158
They get to create fun out of nothing.

482
00:39:34,158 --> 00:39:35,442
Sure.

483
00:39:35,442 --> 00:39:35,992
You have to.

484
00:39:35,992 --> 00:39:38,233
a parent, I think remembering that.

485
00:39:39,118 --> 00:39:44,501
I always, you know, even when I was a kid, I would fiddle with stuff.

486
00:39:44,501 --> 00:39:50,985
So back in high school, in high school biology, I was in ace at dissection.

487
00:39:51,085 --> 00:40:00,290
And that was a time when, you know, Christiane Barnard and Denton Cooley and others were
doing their first heart transplants.

488
00:40:00,331 --> 00:40:02,111
So I said, you know, this is cool.

489
00:40:02,312 --> 00:40:04,653
I could do a heart transplant on frogs.

490
00:40:04,913 --> 00:40:06,514
So one...

491
00:40:06,794 --> 00:40:17,887
one evening I found two unfortunate frogs in my backyard and I found that it was much
easier to take a frog apart than to put it back together.

492
00:40:20,987 --> 00:40:25,464
I mean, I feel the same way about my VCR when I was a kid, but man, a frog, that's...

493
00:40:26,082 --> 00:40:29,237
Well, so, you know, I've got other stories like that too.

494
00:40:29,237 --> 00:40:40,685
You once I opened my watch, I had an old mechanical watch from back in the 1960s and, you
know, I tried putting it back together and I had enough pieces left over for the second

495
00:40:40,685 --> 00:40:41,526
watch.

496
00:40:44,115 --> 00:40:45,195
For sure.

497
00:40:46,700 --> 00:40:48,173
How does that apply?

498
00:40:48,173 --> 00:40:50,088
I feel like that happens in software too.

499
00:40:50,088 --> 00:40:55,418
In software, I go to refactor something and I'm like, why do I have so much left over
here?

500
00:40:56,046 --> 00:40:58,591
sure, that's absolutely true.

501
00:40:58,591 --> 00:41:05,422
It's understanding how things work, whether it's software or whether it's hardware or
whether it's an animal.

502
00:41:05,422 --> 00:41:08,566
It's understanding what makes things tick.

503
00:41:08,742 --> 00:41:11,504
So help me understand YottaDB a little bit, right?

504
00:41:11,504 --> 00:41:15,506
Like that's the code base and the company that you founded, you've been working on for so
long.

505
00:41:15,587 --> 00:41:19,529
Like on the website, the fastest key value database, right?

506
00:41:19,529 --> 00:41:21,821
Like what makes it that fast?

507
00:41:21,821 --> 00:41:24,042
Why does it function in that way?

508
00:41:24,042 --> 00:41:26,854
And why is that something you've been doing for so long?

509
00:41:27,512 --> 00:41:35,806
So, I mean, what makes the RDB so fast, I think, is just it's an obsession with speed.

510
00:41:35,806 --> 00:41:37,747
I mean, actually, speed is second.

511
00:41:37,747 --> 00:41:40,327
The first thing is it's an obsession with correctness.

512
00:41:40,788 --> 00:41:47,471
Because if the software doesn't have to be right, then you can make it arbitrarily fast.

513
00:41:47,471 --> 00:41:51,563
oh So, know, speed comes first.

514
00:41:51,563 --> 00:41:53,613
But we do obsess over speed.

515
00:41:55,678 --> 00:42:02,660
And even from one release to the next, if you see any kind of slowdown, we go through, we
analyze why it is that way.

516
00:42:02,660 --> 00:42:06,041
It is just something that we do naturally.

517
00:42:06,041 --> 00:42:16,004
And so right now, the next release, for example, we're looking at rewriting the garbage
collector oh just because that's a potential opportunity.

518
00:42:16,324 --> 00:42:18,804
So I think that that's where it comes from.

519
00:42:18,804 --> 00:42:24,736
And again, the way that it was developed, it was developed at a time when computers were

520
00:42:25,390 --> 00:42:27,952
know, a thousand times slower than they are today.

521
00:42:28,614 --> 00:42:36,992
And when you do that, then you naturally focus a lot more on performance and that kind of
carries through in the code base all the way to today.

522
00:42:38,438 --> 00:42:45,602
Now key value data stores tend, like I know a lot of people that treat them redis, right?

523
00:42:45,602 --> 00:42:46,292
It's a cache.

524
00:42:46,292 --> 00:42:47,943
I don't actually care about the data.

525
00:42:47,943 --> 00:42:49,704
It's throw away information.

526
00:42:49,704 --> 00:42:51,385
I could rehydrate this somewhere else.

527
00:42:51,385 --> 00:43:00,469
But then I also think on the other side of that on things like Kubernetes and etcd,
another key value database that's really important, but also uh intentionally.

528
00:43:01,264 --> 00:43:03,908
fault tolerant for being distributed, right?

529
00:43:03,908 --> 00:43:07,414
We want something that's distributed so that we don't ever lose information.

530
00:43:07,414 --> 00:43:13,113
And on the other end, we have this, I don't really care about it, I just want it in RAM
and it can go away at any time.

531
00:43:13,113 --> 00:43:16,117
Where does YottaDB sit in that sort of scale?

532
00:43:16,635 --> 00:43:26,560
a third dimension to that, which is that you do care about the information, but in our
case, we also care about, so say, let's say, know, distributed database.

533
00:43:27,101 --> 00:43:31,603
You're never going to get high transaction performance with the database.

534
00:43:31,954 --> 00:43:38,207
In our case, transaction performance is absolutely important for high-end banking systems.

535
00:43:38,788 --> 00:43:42,130
And at the same time, you know, having the data be

536
00:43:43,855 --> 00:43:47,477
The integrity of the data is is bad about.

537
00:43:47,857 --> 00:44:00,096
So we have different techniques for doing that, basically replicating in real time to
different replicas, but having one system be essentially the system of truth at any given

538
00:44:00,096 --> 00:44:01,066
instant.

539
00:44:03,168 --> 00:44:07,071
comparing it to Redis, though, people often use Redis as a cache.

540
00:44:07,071 --> 00:44:11,274
Now, in our case, we're faster than Redis and we're a database, so you don't really need a
cache.

541
00:44:11,274 --> 00:44:12,294
uh

542
00:44:12,376 --> 00:44:14,887
You just use the database directly.

543
00:44:15,628 --> 00:44:22,913
And I think there was a question that you had when you didn't actually articulate it, but
you were kind of wondering about key value databases.

544
00:44:22,913 --> 00:44:27,256
It's important to remember that the very first databases were actually key value
databases.

545
00:44:27,377 --> 00:44:30,898
In fact, the very first database was a key value database.

546
00:44:31,179 --> 00:44:41,536
It was developed by Rockwell and IBM for the Saturn V Apollo to manage the bill of
materials for the moonshot.

547
00:44:41,686 --> 00:44:45,333
and it was developed in the 1960s and that was a key value database.

548
00:44:45,333 --> 00:44:48,408
And that database, by the way, is still running today.

549
00:44:48,408 --> 00:44:50,861
It's an IBM product called IMS.

550
00:44:51,864 --> 00:44:55,516
And, you know, and then, so go ahead.

551
00:44:55,516 --> 00:44:57,868
why did they write a database?

552
00:44:57,868 --> 00:44:59,719
Like I don't actually know the history.

553
00:44:59,719 --> 00:45:05,364
like I imagine we start with files on disk and we just say, we can't have two things right
to the file on disk.

554
00:45:05,364 --> 00:45:11,239
And so we need something else to handle deletions and whatever, like locking, whatever the
case may be.

555
00:45:11,239 --> 00:45:16,874
And so at some point we like changed from saying this file that I'm writing to is now a
database.

556
00:45:19,522 --> 00:45:22,644
Well, it's not just the access control.

557
00:45:22,644 --> 00:45:27,768
It's also the fact that you need to search and retrieve data.

558
00:45:28,749 --> 00:45:34,253
So yes, in theory, you can just take a flat file and any flat file as a database.

559
00:45:35,194 --> 00:45:44,741
But on the other hand, finding information in that flat file or updating information in
the flat file can be challenging if it's a big file.

560
00:45:44,741 --> 00:45:46,622
And that's where databases come in.

561
00:45:49,307 --> 00:45:51,368
At some point we, I mean, there's still files on disk, right?

562
00:45:51,368 --> 00:46:02,094
At some point they're like, there's bits on a disk, but how I index that's how I make sure
I can quickly access the right information and update or write new information.

563
00:46:02,466 --> 00:46:04,047
That's exactly what the database is.

564
00:46:04,047 --> 00:46:12,170
And ultimately, as a database developer, we rely on the integrity of the underlying file
system.

565
00:46:12,670 --> 00:46:17,682
So if the file system gets corrupted somewhere, then we can't really use it.

566
00:46:17,682 --> 00:46:22,474
Because when we write data to the file system, we expect to get it back.

567
00:46:23,814 --> 00:46:28,578
I bet you have some stories there about file systems that didn't give you that data.

568
00:46:28,578 --> 00:46:35,684
Well, mean, today there are only two file systems that we consider fully supported, EXT4
and XFS.

569
00:46:36,326 --> 00:46:41,630
So we consider F2FS kind of supportable, but not necessarily supported.

570
00:46:43,052 --> 00:46:53,382
we tell our customers we don't support ButterFS, we don't support ZFS, we don't support
NFS, because we have found in our testing that they're not always reliable.

571
00:46:53,382 --> 00:47:06,576
That's fascinating because I always think of ZFS and ButterFS as having more checksums and
more, you know, they have protections against bitrot and all these things that XFS and

572
00:47:06,576 --> 00:47:08,278
EXT4 doesn't have.

573
00:47:09,602 --> 00:47:16,873
Well, in our testing, we routinely run, we've got a couple dozen computers out here, we're
constantly testing.

574
00:47:17,335 --> 00:47:24,736
And we have found situations where ZFS and buttered AFS basically don't give us the data
that we expect.

575
00:47:25,062 --> 00:47:26,202
Hmm.

576
00:47:26,442 --> 00:47:27,863
That's fascinating.

577
00:47:28,724 --> 00:47:39,715
even thinking back on before we had journaled file systems, with whatever you were
writing, you hope it wrote to disk before the power goes off, right?

578
00:47:39,715 --> 00:47:45,614
Like there's a lot of situations that we've come a long way in those areas to make sure
file systems were pretty reliable.

579
00:47:45,614 --> 00:47:58,894
Oh, actually, I do have a story on that, in fact, which is that when the upstream code
base first went into production back in like 1986, someone accidentally at a data center

580
00:47:58,894 --> 00:48:02,353
kicked out the power cable of the computer system.

581
00:48:03,014 --> 00:48:10,354
And they found that there was a bug and there was like two or three weeks of data which
had not been recorded in the database.

582
00:48:10,974 --> 00:48:15,010
So the vendor actually sent all of the team down to

583
00:48:15,010 --> 00:48:19,676
the customer site and unfortunately that time they still had the paper records.

584
00:48:19,798 --> 00:48:29,734
So basically everyone had this crash project to go put the paper records back in the
database and also fix the bug in the database so that everything got written out to disk.

585
00:48:34,082 --> 00:48:45,239
We started this conversation talking about having difficulty with things occasionally and
still trying to learn new things and banging your head on the wall, trying to figure out

586
00:48:45,239 --> 00:48:46,890
like how things are working.

587
00:48:46,890 --> 00:48:53,334
What are the things today that you might still struggle with learning or trying to pick up
when you're building it?

588
00:48:55,054 --> 00:48:59,914
Well, mean, certainly one of the things that I'm struggling with right now is AI.

589
00:49:02,014 --> 00:49:06,094
I find that it's useful.

590
00:49:06,894 --> 00:49:18,134
sometimes when I, you like I had to replace my laptop battery over the weekend and I was
kind of wondering why the old battery died after just two years.

591
00:49:18,194 --> 00:49:24,526
And I went to do some research and found that, you know, I shouldn't charge it to more
than 80 % if I...

592
00:49:24,526 --> 00:49:27,488
So then I said, how do I keep it within that limit?

593
00:49:27,488 --> 00:49:31,702
And basically I went to, I asked a couple of different AI models.

594
00:49:31,702 --> 00:49:37,837
Ultimately I find DeepSeq to be the easiest one to use for me, but it gave me good
information.

595
00:49:38,338 --> 00:49:47,425
But where to have the problem is understanding what it's doing, what it's been trained on,
how is it giving me that answer?

596
00:49:48,206 --> 00:49:53,760
And it doesn't seem to know its limits sometimes.

597
00:49:54,737 --> 00:49:55,309
Yeah, for sure.

598
00:49:55,309 --> 00:50:00,078
It's so confident in the information it gives without knowing where the boundaries of

599
00:50:00,078 --> 00:50:07,438
Right, then something like my laptop battery, not particularly important.

600
00:50:07,438 --> 00:50:13,138
I also asked it about recipes for pasta sauce when I was cooking dinner a couple of nights
ago.

601
00:50:13,138 --> 00:50:14,938
It did all that well.

602
00:50:14,978 --> 00:50:27,570
But then when you think about using AI to predict, some people are trying to do this,
someone who is going to commit a crime before they commit a crime.

603
00:50:27,570 --> 00:50:28,224
you

604
00:50:28,224 --> 00:50:39,668
or you have face recognition software where it's been trained well on, let's say, white
males, but can't really tell other people apart.

605
00:50:39,668 --> 00:50:42,399
And there are enough cases of mistaken identity.

606
00:50:42,539 --> 00:50:46,700
So those are the kinds of things where I'm certainly concerned.

607
00:50:46,700 --> 00:50:52,542
I don't have a good grasp on it, and I don't feel that we as a society have a good grasp
on it.

608
00:50:54,507 --> 00:51:01,643
or at least the people that have any sort of graphs on it have uh a vested interest in
making sure other people don't understand it, right?

609
00:51:01,643 --> 00:51:04,848
Because as soon as it's not magic and it's just a technology.

610
00:51:05,554 --> 00:51:10,254
they kind of lose power of being able to train on whatever data they want.

611
00:51:10,254 --> 00:51:14,794
Because right now, looking at what they train on is copyrighted material.

612
00:51:14,794 --> 00:51:15,974
And they can spit out that copyright.

613
00:51:15,974 --> 00:51:20,274
They're like, oh, no, we have to have this because you don't understand how AI works.

614
00:51:20,274 --> 00:51:26,154
And then at the end of the day, it's like, no, that's what Google did back in the day,
where they just said, we're going to scan every book.

615
00:51:26,154 --> 00:51:30,634
And then when you sue us to say we can't scan every book, we're going to say, oh, OK,
we'll stop.

616
00:51:30,634 --> 00:51:31,794
But we already have the books.

617
00:51:31,794 --> 00:51:32,574
We already have the data.

618
00:51:32,574 --> 00:51:33,554
We're fine.

619
00:51:33,554 --> 00:51:34,534
We'll keep.

620
00:51:36,184 --> 00:51:45,937
Well, and the other thing is AI certainly has had a whole bunch of, know, hucksters come
out there that say we can, you know, here's all this magic that we can do and trust us

621
00:51:45,937 --> 00:51:47,969
it's going to work.

622
00:51:48,464 --> 00:51:49,240
Yeah.

623
00:51:50,931 --> 00:52:00,251
Yeah, and again, a vested interest in making that money and making those promises that
this will grow forever and they can do anything they can.

624
00:52:00,251 --> 00:52:07,251
But at the end of the day, LLMs, at least to some extent, are fancy databases.

625
00:52:07,904 --> 00:52:08,636
sure.

626
00:52:09,356 --> 00:52:13,872
and rely on a lot of that data uh structured in certain ways.

627
00:52:13,872 --> 00:52:17,987
uh I've been learning a lot about vector databases and just like, what do they do?

628
00:52:17,987 --> 00:52:19,178
How do they store data?

629
00:52:19,178 --> 00:52:20,500
Why is it important?

630
00:52:20,500 --> 00:52:26,006
And why did we even need a different type of database for this sort of thing?

631
00:52:26,227 --> 00:52:28,089
So yeah, that's very fascinating.

632
00:52:28,089 --> 00:52:28,727
oh

633
00:52:28,727 --> 00:52:36,053
about because if you think about YaraDB, we have a, at the core, core technology is a key
value data store.

634
00:52:36,894 --> 00:52:40,296
And on top of that, we've, for example, we have a SQL layer.

635
00:52:40,357 --> 00:52:44,780
Ultimately, a key value data store is the most general type of data store.

636
00:52:45,281 --> 00:52:47,093
And so we can put SQL on top of it.

637
00:52:47,093 --> 00:52:53,167
I've been thinking about what would it take to put a vector database layer on top of it?

638
00:52:53,708 --> 00:52:56,200
And which one would we want to be compatible?

639
00:52:57,944 --> 00:53:07,309
So it certainly is something that ultimately, when you have an AI system, there's a large
vector database somewhere that's actually storing the data.

640
00:53:07,551 --> 00:53:11,988
And they have some efficient access to that data to make it work.

641
00:53:13,254 --> 00:53:16,221
Do you think it's a fad?

642
00:53:16,364 --> 00:53:18,368
AI vector databases?

643
00:53:18,988 --> 00:53:19,918
No, it's not a fad.

644
00:53:19,918 --> 00:53:27,240
I think that what will happen is eventually people will realize what the limits are.

645
00:53:27,240 --> 00:53:30,822
oh But they're still useful.

646
00:53:30,822 --> 00:53:33,422
They've proved their usefulness.

647
00:53:34,483 --> 00:53:36,923
And once something is useful, it's not going to go away.

648
00:53:36,923 --> 00:53:39,624
It's just that people will keep pushing it.

649
00:53:39,624 --> 00:53:43,995
when it gets to the limits, then people will move on to something else.

650
00:53:43,995 --> 00:53:45,926
I kind of like search engines, right?

651
00:53:46,658 --> 00:53:50,139
When search engines were great, people used them, they kept pushing them.

652
00:53:50,279 --> 00:54:02,472
Then they said, with search engines, can now, Google came along with Gmail, and they came
along with, and some things worked like, Yahoo came up with the idea of a portal, but that

653
00:54:02,472 --> 00:54:05,263
didn't really take off from a business point of view.

654
00:54:05,383 --> 00:54:13,635
But ultimately, we got to a point where people realized the limits of search engines, and
then comes along AI, and that's something that, if you think about it, it's like something

655
00:54:13,635 --> 00:54:16,896
that sits on top of a search, on top of many search engines.

656
00:54:17,510 --> 00:54:18,795
Yeah, I've been thinking about that a lot.

657
00:54:18,795 --> 00:54:23,470
think that to me, AIs are kind of a search engine 2.0, right?

658
00:54:24,082 --> 00:54:30,002
where search engines, looking back even further, like early internet days, right?

659
00:54:30,002 --> 00:54:38,302
Like they were manually curated lists of websites that, know, Yahoo kept and said like,
here's the websites you should go to to find some information.

660
00:54:38,302 --> 00:54:42,342
And then AltaVista and stuff like, oh, you can dynamically find this information.

661
00:54:42,342 --> 00:54:48,322
And we'll make that a little better by making you, give you the most reputable source for
that information, right?

662
00:54:48,322 --> 00:54:49,562
That's where we kind of ended with Google.

663
00:54:49,562 --> 00:54:53,822
And I feel like the demise of Google as a search engine specifically,

664
00:54:54,276 --> 00:55:02,582
place for AI being the next search engine where I don't actually care on what one website
says about a pasta sauce recipe.

665
00:55:02,582 --> 00:55:11,879
I care about what a hundred websites say about a pasta recipe and just give me the
grouping of that thing and right now they're obviously very wrong.

666
00:55:11,879 --> 00:55:13,589
There's been plenty of cases where

667
00:55:13,946 --> 00:55:23,915
Gemini says put glue on your pizza and do things that are completely absurd but in general
in that probably is like the weights for those systems are like Over indexing on what they

668
00:55:23,915 --> 00:55:33,114
think has more authority like reddit reddit has a lot of authority because there's lots of
people there but it's also very sarcastic which is where that stuff usually comes from and

669
00:55:33,114 --> 00:55:35,416
so I think that that

670
00:55:35,663 --> 00:55:40,948
AI systems eventually become that sort of, don't want to go to one link.

671
00:55:40,948 --> 00:55:51,078
I want to get a summary of 50, the next three pages of links, and you just tell me what
they all said together in one summary and just give me the grouping of it, right?

672
00:55:51,078 --> 00:55:58,325
Like the nine out 10 doctors recommend sort of thing, instead of going like, I'm going to
pick this website for that specific thing.

673
00:55:58,325 --> 00:56:00,407
I just want to know, like, what do they generally all think?

674
00:56:00,558 --> 00:56:02,139
Sure, in fact that's exactly it.

675
00:56:02,139 --> 00:56:04,739
If you go back to, you know, mentioned AltaVista.

676
00:56:06,140 --> 00:56:16,783
One of the problems with AltaVista, at least for me, that when I switched from AltaVista
to Google, was like I do a search and it would come back and say here's 60,000, you know,

677
00:56:16,983 --> 00:56:19,224
websites that answer your question.

678
00:56:19,224 --> 00:56:26,475
And Google on the other hand, because of the page rank algorithm, you know, you probably
got 10 links which were useful.

679
00:56:26,475 --> 00:56:29,018
even the fact that Google put like they still have it.

680
00:56:29,018 --> 00:56:30,029
I'm feeling lucky, right?

681
00:56:30,029 --> 00:56:33,383
If I go to Google, I haven't been to Google.com for so long.

682
00:56:33,383 --> 00:56:35,685
just yeah, the second button, I'm feeling lucky.

683
00:56:35,685 --> 00:56:40,300
Like that was the unique thing of like, I will give you the top.

684
00:56:40,462 --> 00:56:49,806
It still exists on their website today because you're right they over index where Yahoo
said we can curate a bunch of lists But that was a limitation on people managing or

685
00:56:49,806 --> 00:56:55,189
reading stuff altavista came along said hey our database is so big You can't even believe
it.

686
00:56:55,189 --> 00:56:59,511
We will give you a hundred thousand links for this thing and Google said I'm gonna give
you one Right.

687
00:56:59,511 --> 00:57:06,014
I used to Google stuff and say I'm feeling like no one does that today I don't know why
that button still exists But that was the that was the selling point was like we could

688
00:57:06,014 --> 00:57:10,856
take all of the database of we do have a hundred thousand things But we're gonna give you
the one

689
00:57:10,876 --> 00:57:19,590
right thing and and maybe Jem and I should have just been the I'm feeling lucky button all
over again right like that's we're all back to that point of like I don't care about the

690
00:57:19,590 --> 00:57:24,282
one link though I care about the grouping of what all the links thought together

691
00:57:24,504 --> 00:57:29,808
How do you know the Gemini isn't that I'm feeling lucky but they put a language thing in
front of it?

692
00:57:29,808 --> 00:57:32,560
Yeah, it very much, it could be.

693
00:57:34,422 --> 00:57:36,524
Batch guard, this has been so much fun.

694
00:57:36,524 --> 00:57:41,629
Thank you for coming on the show and talking to us about just everything.

695
00:57:41,629 --> 00:57:46,524
Like all of your experiences, what you've been doing with YottaDB, uh why it matters.

696
00:57:46,524 --> 00:57:54,071
Like why is it still even in the year 2025, like why high performance flexible databases
are just kind of important.

697
00:57:54,071 --> 00:57:55,342
That's been so much fun.

698
00:57:55,534 --> 00:57:57,117
oh Thank you for inviting me.

699
00:57:57,117 --> 00:57:59,669
It's been fun talking with with Otto Menu.

700
00:57:59,846 --> 00:58:02,354
Yeah, thanks so much and thank you everyone for listening.

701
00:58:02,354 --> 00:58:03,962
We will talk to you again soon.