0:00

What is the status serverless computing and

0:02

Python in 2024? What

0:04

are some of the new tools and best practices? Well,

0:07

we're lucky to have Tony Sherman, who

0:09

has a lot of practical experience with

0:12

serverless programming on the show. This

0:14

is Talk Python to Me, Episode 458 recorded January 25th, 2024.

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for free at mailtrap.io. Tony,

1:24

welcome to Talk Python to Me. Thank you.

1:26

Thanks for having me. Fantastic to have you

1:28

here. It's going to be really fun to

1:30

talk about serverless. The joke with the cloud

1:32

is, I know you call it the cloud, but

1:34

it's really just somebody else's computer, but we're not

1:36

even talking about computer, we're just talking about functions.

1:39

Maybe someone else's functions. I don't know, we're going

1:41

to find out. Yeah, I actually saw a recent

1:43

article about server free. Somebody

1:45

trying to move completely.

1:49

Because as you might know, serverless

1:51

doesn't mean actually no servers. Of

1:53

course, server free. We

1:56

could just get the thing to run on

1:58

the BitTorrent network. Got it. Okay.

2:02

I don't know. I don't know. We'll figure it out.

2:04

But it's going to be super fun. We're going to

2:06

talk about your experience working with serverless.

2:08

We'll talk about some of the choices people

2:10

have out there and also some of the

2:13

tools that we can use to do things

2:15

like observe and test our serverless code. For

2:17

that though, tell us a

2:19

bit about yourself. Sure. So I'm

2:21

actually a career changer. So I

2:23

worked in the cable industry for

2:26

about 10 years and

2:28

doing a lot of different things

2:30

from installing K, knock out the

2:32

door cable guy to working on

2:34

more of the outside plant. But

2:36

at some point, I was

2:38

seeing limits of career path there. And

2:41

so my brother-in-law is a software engineer

2:43

and I had already started going

2:45

back to school, finishing my degree and I was

2:47

like, okay, maybe I should look into this. And

2:49

so I did, I took an intro to programming

2:51

class. It was in Python. And that

2:54

just, yeah, led me down

2:56

this path. So now for the past four

2:58

years or so, been working professionally in the

3:00

software world, started out in a QA role

3:03

at an IoT company. And

3:05

now I'm doing a lot of serverless

3:07

programming in Python these days. Second company

3:09

now, but that does

3:11

some school bus safety products. So a

3:13

lot of Python, a lot of serverless.

3:16

Well, serverless and IoT feel like they

3:18

go pretty hand in hand. Yeah. Another

3:20

thing is with serverless is

3:22

when you have very like spiky traffic,

3:24

like if you think about school buses,

3:26

we have a lot coming on. There's

3:28

a choice a day. Bimodal, exactly. Like

3:30

the 8am shift and then the 2.30 to 3

3:33

shift. Yeah. Yeah. That's

3:35

a really good use case for serverless. Something

3:37

like that. Are you

3:39

enjoying doing the programming stuff, say the

3:42

cable stuff? Absolutely. Sometimes I live in

3:44

Michigan. So I look outside and look

3:46

at the snow coming down or these

3:49

storms. And yeah, I just, yeah, I really, some

3:51

people like you don't miss being outside. Maybe every

3:53

once in a while, but I can go walk

3:55

outside on a nice day. You

3:58

can choose to go outside. not made

4:00

to go outside and have a sleet

4:02

or rain. Yeah, absolutely. We just had

4:04

a mega storm here and just the

4:06

huge tall trees here in Oregon just

4:08

fell left and right. And

4:11

there's in every direction that I look, there's

4:13

a large tree on top of one of

4:15

the houses of my neighbors. I know

4:17

maybe a house to over, but it just took out

4:19

all the everything that was a cable in the air

4:21

was taken out. So it's just been a swarm of

4:23

people who are out in 13 degree

4:26

Fahrenheit negative nine Celsius weather.

4:29

And I'm thinking, I'm not really choosing to

4:31

be out there today probably. Right. A lot

4:33

of people probably know what serverless is, but

4:35

I'm sure there's a lot who are not

4:37

even really aware of what serverless programming is,

4:40

right? Let's talk about

4:42

what's the idea. So what's the Zen of

4:44

this? Yeah, I made the joke that serverless

4:46

doesn't mean there are no servers, but and

4:48

there's hopefully I don't butcher

4:50

it too much, but it's more functions

4:53

as a service. There's other things that

4:55

can be serverless too. There's serverless databases

4:57

or a lot of different services

5:00

that can be serverless, meaning you don't have

5:02

to think about like how to operate them,

5:04

how to think about scaling them up. You

5:07

don't have to spin up VMs or

5:09

Kubernetes clusters or anything. You don't have

5:11

to think about that part. It's just

5:13

your code that goes into it. Yeah.

5:17

Serverless functions are probably what people are most

5:19

familiar with. And that's, I'm sure what we'll

5:21

talk about most today, but that's really

5:23

the idea. You don't have to manage the server.

5:26

Sure. And that's a huge barrier. I

5:29

remember when I first started getting

5:31

into web apps and programming and

5:33

then another level when I got

5:35

into Python because I had not

5:37

done that much Linux work, getting

5:39

stuff up running, it was

5:41

really tricky. And then having the

5:43

concern of is it secure? How

5:46

do I patch it? How do I back it up?

5:48

How do I keep it going? All of those things

5:51

are non-trivial, right? There's a lot to think about.

5:53

And if you work at an

5:56

organization, it's probably different everywhere you go

5:58

to that they, you know. how they

6:00

manage their servers and things. So putting some

6:02

stuff in the cloud kind of brings some

6:04

commonality to it too. Like you can learn

6:07

how the Azure cloud or Google cloud or

6:09

AWS, how those things work and have

6:11

some common ground too. I also

6:13

feel more accessible to the developers in a

6:16

larger group in the sense that not a

6:18

DevOps team that kind of takes care of

6:20

the servers or a production engineers where you

6:23

hand them your code. It's a little closer

6:25

to just, I have a function and then

6:27

I get it up there and it continues

6:29

to be the function. And that is a

6:32

different mindset too. You see it all the

6:34

way through from writing your code to deploying

6:36

it without maybe an entire DevOps team that

6:39

you just say, here you go

6:41

deploy this. In my world, I

6:43

mostly have virtual machines. I've moved

6:46

over to a Docker

6:48

cluster. I think I've

6:50

got 17 different things running in the

6:52

Docker cluster at the moment, but both

6:54

of those are really different than serverless.

6:57

So it's been working well for me, but

6:59

when I think about serverless, let me know

7:01

if this is true. It feels like you

7:04

don't need to have as much of a

7:06

Linux or server or

7:08

sort of an ops experience to

7:11

create these things. You could probably get away

7:13

with like almost none, right? Like at the

7:16

simplest form, like with

7:18

AWS for instance, their

7:20

Lambda functions, you can,

7:23

and that's the one I'm most familiar with.

7:25

Forgive me for using them as an example

7:27

for everything. There's a lot of different serverless

7:29

options, but you could go into

7:32

the AWS console and you could

7:34

actually write your Python code right

7:36

in the console, deploy that. They

7:38

have functioned URLs now. So you

7:40

could actually have, I mean,

7:43

within a matter of minutes, you could have

7:45

a serverless function set up. AWS Lambda, right?

7:47

That's the one. Lambda being, I

7:50

guess a simple function, right? We have Lambdas in

7:52

Python. They can be one line. Sure

7:54

you can have more than one line in

7:57

the AWS Lambda, but there are limitations though

7:59

with Lambda. to that you that are

8:01

definitely some pain points that I ran into.

8:03

Oh really? Okay, what are some of the

8:05

limitations? Package size is

8:07

one. So if you start thinking

8:10

about all these amazing packages on

8:12

PyPI, you do have to start

8:14

thinking about how many you're going to bring in. And I

8:17

don't know the exact limits off the

8:19

top of my head but it's pretty

8:21

quick Google search on their package size.

8:23

It might be like 50 megabytes zipped

8:26

but 250 when you decompress it to

8:28

do a zip base then they do

8:30

have containerized lambda functions that

8:32

go up to a 10 gig limit

8:34

so that helps but interesting. Okay, those

8:36

ones used to be less performant but

8:38

they're catching up to where they're that

8:40

was really on something called cold starts.

8:42

But they're getting I think pretty close

8:44

to it not not being a very

8:46

big difference whether you dockerize or zip

8:48

these functions but but yeah, so when

8:50

you start just like pip installing everything,

8:52

you got to think about how to

8:55

get that code into your function and

8:57

how much it's going to

8:59

bring in. So yeah, that definitely was a limitation

9:02

that had to quickly

9:04

learn. Yeah, I guess it's

9:06

probably trying to do pip install

9:08

dash R effectively to start

9:11

the and you can't go overboard with this.

9:13

Yeah, when you start bringing in packages, maybe

9:15

like some of the scientific packages, you're definitely

9:17

going to be hitting some size limits. And

9:20

with the containerized ones, basically, you get probably

9:22

give it a Docker file and a command

9:24

to run in it. And it can build

9:26

those images before and then just execute and

9:28

just do a Docker run more or less

9:31

how those ones work is you

9:33

store an image on like

9:35

their container registry Amazon's is it ECR,

9:37

I think. And so then you pointed

9:39

at that and yeah, it'll execute

9:42

your like handler function when that

9:45

when the lambda gets called. Yeah,

9:47

excellent. Out the audience, Kim says

9:49

AWS does make a few packages

9:51

available directly just by default and

9:53

lambda. That's nice. Yeah, Bodo, which

9:56

if you're dealing with AWS and

9:58

Python, you're using the Bodo. photo,

10:00

package, and yeah, that's included for you.

10:02

So that's definitely helpful in any of

10:04

their transitive dependencies would be there. I

10:07

think photo used to even include like requests,

10:10

but then I think they eventually dropped

10:12

that with some SSL stuff. But

10:15

yeah, you definitely, you

10:17

can't just like pip install anything and not think of

10:19

it unless, depending on how you package

10:22

these up. So sure that makes sense. Yeah, of

10:24

course, they would include their own Python libraries, right?

10:27

Yeah, it's not exactly small. I think

10:29

like photo core used to be like 60 megabytes,

10:32

but I think they've done some work to

10:34

really get that down. Yeah, that's not too

10:36

bad. I feel like photo

10:38

core, photo three, those are constantly

10:40

changing, like constantly. Yeah, as

10:43

fast as AWS add services, that

10:46

they'll probably keep changing quickly. So I

10:48

feel like those are auto generated, maybe

10:50

just from looking at the way the

10:52

API looks in the way they look

10:54

written. Yeah, that's probably the case. Yeah.

10:57

And then you can do that with like their, their infrastructure

10:59

as code CDK, it's all like TypeScript originally,

11:01

and then you have your Python bindings for

11:04

it. And so when you see a change,

11:06

it doesn't necessarily mean, oh, there's a new

11:08

important aspect added. It's probably just, I don't

11:10

know, people have actually pulled up the console

11:12

for AWS, but just the amount of services

11:15

that are there. And then think each one

11:17

of those has its own full API, like

11:19

a little bit of the one of the

11:21

machines. So we regenerated it. But it might

11:23

be for some part that you never call,

11:25

right? Like you might only work with S3

11:28

and it's only changed. I don't know, EC2

11:30

stuff. Right? Exactly.

11:33

Indeed. All right. Let's talk

11:35

real quickly about some of the places

11:37

where we could do serverless, right? We've

11:39

mentioned AWS Lambda. Yep. Maybe

11:41

touch on just 1 million requests free per

11:44

month. That's pretty cool. Getting like jumping into

11:46

AWS sometimes sounds scary, but they have

11:48

a pretty generous free tier. Definitely

11:50

do your research on some of the security

11:52

of this. But yeah, you can, a million

11:54

requests free per month. You probably have to

11:57

look into that a little bit because it's,

11:59

you have your your memory configurations too.

12:01

So there's probably, I don't

12:04

know exactly how that works within their free

12:06

tier, but your charge, like with Lambda at

12:08

least, it's your like invocation

12:10

time and memory and also amount

12:13

of requests. So yeah. I've

12:15

always confused when I look at that and go, okay,

12:18

with all of those variables, is that

12:20

a lot? I know it's a lot,

12:22

but it's hard for me to conceptualize. I use a little

12:24

more memory than I thought. So it costs, wait a minute,

12:26

how do I know how much memory I use? What

12:29

does this mean in practice? Yeah, it's built

12:31

by how you configure it too. So if

12:33

you say I need a Lambda with 10

12:35

gigs of memory, you're being built at that

12:37

like 10 gigabyte price

12:41

threshold. There is a really cool tool

12:43

called AWS Lambda Power Tuner. Yeah,

12:49

what that'll do is you can, it

12:51

creates a state machine in AWS. Yeah, I

12:53

think I did send you a link to

12:56

that one. So the Power Tuner will create

12:58

a state machine that invicates your Lambda

13:00

with several different memory configurations. And you

13:02

can say, I want either the best

13:05

cost optimized version or the best performance

13:07

optimized version. And that'll tell you, like

13:09

it'll say, okay, yeah, you're best with

13:11

a Lambda configured at 256 megabytes for

13:14

memory. Sorry, yeah, for the link, it's,

13:16

this is Power Tools. This is a

13:18

different, amazing package. Yeah, I got it,

13:20

I got it, yeah. Maybe I didn't

13:23

send you the Power Tuner. Okay,

13:25

sorry, that's a little bit. It's news to me. I'll look

13:27

at the date. Okay, sorry, yeah. And they

13:30

have similar names. There's only so many ways

13:32

to describe stuff. Right, yeah, okay, they have

13:34

it right in their AWS. This one probably.

13:36

Yep, and it is an open source package.

13:38

So there's probably a GitHub link in there,

13:40

but yeah. And this will tell you like

13:43

the best way to optimize your Lambda

13:45

function, at least as far as memories

13:47

concerns. Yeah, really good tool. It gives

13:49

you a visualization, it gives you a

13:51

graph that will say, okay, here's where

13:53

cost and performance meet. Yeah, that's cool.

13:55

It's really excellent for figuring that out.

13:57

Yeah, at least in, in. AWS

14:00

land. I don't know if some of the

14:02

other cloud providers have something similar to this,

14:04

but yeah, it's definitely a really

14:07

helpful tool. I'm confused and I've been doing cloud

14:09

stuff for a long time when I look at

14:11

it. There's some interesting things here. So you can

14:13

actually have a lambda invocation that

14:15

costs less with a higher memory

14:17

configuration because it'll run faster. So

14:19

you're, I think lambda bills like

14:21

by the millisecond now, you can

14:24

actually because it runs faster, it

14:26

can be cheaper to run. That

14:28

explains all the rust that's been

14:30

getting written. Yeah. Yeah. There's

14:33

a real number behind this. I mean, we

14:35

need it to go faster, right? Yeah. I

14:37

think maybe AWS lambda is one of the

14:39

very first ones as well to come on

14:41

with this concept of serverless. I don't know

14:44

for sure, but it probably is. And then

14:46

yeah, your other big cloud providers have them

14:48

and now you're actually even seeing them come

14:50

up with a lot of like Vercel has

14:53

some type of serverless function. I don't know

14:55

what they're using behind it, but it's almost

14:57

like they just put a nicer UI around

15:00

AWS lambda or whichever cloud provider

15:02

that's potentially backing this up.

15:05

But they're just reselling their flavor

15:07

of somebody else's cloud. Yeah. It

15:09

could be because the adversal obviously

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they have a really nice suite

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So, Vercel, some of them people can try, and

16:53

then we've got the

16:55

two other hyperscale clouds, I guess you call

16:57

them Google Cloud has serverless, right? Not sure

16:59

which ones, they might just be

17:02

called Cloud Functions, and Azure

17:04

also has- Cloud Run, they got Cloud

17:06

Run and Cloud Functions. I have no

17:08

idea what the difference is, though. Azure

17:10

also has a serverless product, and I'd

17:12

imagine there's probably even more that we're

17:14

not aware of, but yeah, it's nice

17:16

to not think about setting up servers

17:18

for something, so. I think maybe,

17:21

is it Fast? Yeah, Function as a Service, let's see.

17:23

Yeah. Search for F-A-A-S

17:25

as in instead of PaaS

17:27

or IaaS, right? There's, we've

17:29

got Almeda, Intel, I

17:32

saw that IBM had some. Oh,

17:34

there's also the, we've got DigitalOcean.

17:36

I'm a big fan of DigitalOcean, because I feel

17:39

like their pricing is really fair, and

17:41

they've got good documentation stuff. So they've

17:43

got, sorry, that you can, I

17:46

don't use these, but I suppose you could. Yeah,

17:49

I haven't used these either, but yeah. And

17:52

yeah, as far as cost, especially for

17:54

small, personal projects and things where you

17:56

don't need to have a server on

17:58

all the time, Yeah,

18:00

pretty pretty nice if you have yeah a

18:02

website that you need something server side where

18:04

you gotta have some Python But you don't

18:06

need a server going all the time. Okay,

18:08

like maybe I have a static site But

18:11

then I want this one thing to happen

18:13

if somebody clicks a button something like that

18:15

Yep, you could be completely static but have

18:17

something that is Yeah,

18:19

yeah that one function call that you do need

18:22

Exactly. And then you also pointed out

18:24

that cloudflare has some form of serverless

18:26

I haven't used these either. But yeah,

18:28

I do know that they have some

18:31

type of functions as a service

18:33

as well I don't know what frameworks

18:35

for languages. They let you write them

18:37

in there I use bunny net for

18:39

my CDN. It's just absolutely awesome platform

18:41

I really love it And one of

18:43

the things that they've started offering I

18:46

can get this stupid Completely useless cookie

18:48

banner to go away is they've offered

18:50

of what they call edge compute where

18:52

what you would do I don't know

18:54

where to find it somewhere maybe but

18:57

basically the CDN has a hundred

18:59

and fifteen hundred and twenty points

19:02

of presence all over the world where

19:04

you know This one's close to Brazil.

19:06

This one's close to Australia, whatever and

19:08

but you can actually run serverless functions

19:11

on those things So you deploy them

19:13

so the code actually executes in a hundred

19:16

and fifty hundred fifteen locations Yes, probably cloudflare

19:18

something like that as well. I don't know

19:20

one way or the other AWS has they

19:23

have lambda at edge at

19:25

the edge so that's goes hand in hand with

19:27

their CDN cloud front

19:30

I believe yeah, so they have something

19:32

similar like that where you have a

19:34

lambda that's going to be Performant because

19:36

it's yeah distributed across their CDNs. Yeah

19:38

CDNs. That's a whole nother world. They're

19:40

getting really advanced Yeah, maybe that's a

19:42

different show It's not a show today,

19:44

but it's just the idea of like

19:46

you distribute the compute on the CDN

19:49

It's pretty nice. The drawback is it's just

19:51

JavaScript, which is okay, but it's not the

19:53

same as Python Yeah, I wonder

19:56

if you could do high script interesting

19:58

thought. Yeah. Yeah, we're getting closer and

20:00

closer to Python in the browser. So my

20:02

JavaScript includes this little bit of WebAssembly. I

20:04

don't like semicolons, but go ahead and run

20:06

it anyway. Yeah,

20:08

out in the audience, it looks

20:10

like Cloudflare probably does support Python,

20:13

which is interesting. There's so many

20:15

options out there for serverless functions

20:17

that are, yeah, especially if you're

20:19

already in if you're maybe deploying

20:21

some static stuff over Cloudflare or

20:24

bracel. Yeah, it's sometimes nice

20:26

just to be all in on one

20:28

service. Yeah, it really is. Let's

20:30

talk about choosing serverless over

20:33

other things, right? You actually laid

20:35

out two really good examples, or

20:37

maybe three even with the static

20:39

site example, but bursts of activity

20:41

and really focused time, but really

20:43

low, incredibly low usage other times.

20:45

You're Black Friday traffic, right? Like

20:47

you do not have to

20:50

think of like how many servers to

20:52

be provisioned for something like that. Or

20:54

if you don't know, I think

20:57

there's probably some, I actually know there's been

20:59

like some pretty popular articles about people

21:01

like leaving the cloud. And yeah, if

21:03

you know your scale and exactly what

21:06

you need, you probably can

21:08

save money by just having your own

21:10

infrastructure set up or but yeah, if

21:12

you don't know, or it's very like

21:14

spiky, you don't need to have a

21:16

server that's consuming a lot of power

21:18

running 24 hours a day, you

21:22

can just invoke a function as you need.

21:24

So yeah, there's a super

21:26

interesting series by David Heinemann

21:29

or Hansen of Ruby on Rails

21:31

fame and from base camp about how

21:33

base camp has left the cloud and how they're saving $7

21:36

million and getting better performance over five

21:39

years. Yeah, that's a big investment, right?

21:41

They bought they paid $600,000 for hardware,

21:43

right? Only so many people can

21:48

do that. You got to have that

21:50

running somewhere that with backup power. So

21:52

what they ended up doing for this

21:54

one is they went with some service

21:57

called guest. Okay, hosting, which is like

21:59

white glove. So white labeled

22:01

is the word I'm looking for where it just looks like

22:04

it's your hardware, but they put it into a mega

22:06

data center and they'll have the hardware shipped to

22:08

them and somebody will just come out and install

22:11

it into RACs and go, here's your IP address.

22:14

Like a virtual

22:16

VM in

22:18

a cloud, but it takes three weeks to boot.

22:23

Which is almost diving into it

22:25

because it's almost the exact opposite

22:27

of the serverless benefits.

22:29

It's insane stability. I have

22:31

this thing for five years. We

22:35

have 4,000 CPUs we've installed and we're using

22:37

them for the next five years rather than

22:39

how many milliseconds am I going to run

22:41

this code for? Yeah, it's definitely the far

22:43

opposite. And yeah, maybe serverless

22:45

isn't for every use case, but it's

22:47

definitely a nice tool to have in

22:49

the toolbox. And yeah, you definitely

22:51

even working in serverless, if you're, yeah,

22:54

eventually you're going to need like maybe

22:56

to interact with the database that's got

22:58

to be on all the time. But

23:00

yeah, there's a lot of, it's a good

23:02

tool, but it's definitely not the one

23:05

size fits all solution. So let's talk databases

23:07

in a second. But for when

23:10

does it make sense to say we're going

23:12

to put this, it was supposed I have

23:14

an API, right? That's a pretty, an API

23:16

is a real similar equivalent to what a

23:18

serverless thing is. Like Nicole's API,

23:20

I think is going to happen. I'm going to

23:22

call this function that things going to have. Suppose

23:24

I have an API and it has eight endpoints

23:26

to written a fast API or whatever it is.

23:28

It might make sense to have that as serverless,

23:30

right? You don't want to run a server and all that kind

23:32

of thing. But what if I have an API with 200 endpoints?

23:35

Like, where is the point where there are so many

23:37

little serverless things? I don't even know where to look.

23:40

They're everywhere. Which version is this one? You know what

23:42

I mean? Like, where's that trade off and how do

23:44

you and the people you work with think

23:46

about that? As you start like getting into

23:48

these like microservices, how small do you want

23:51

to break these up? Yeah, there is

23:53

some different thoughts on that. Even like

23:55

a lambda function, for instance, if

23:57

you put this behind an API, you can

23:59

use a single Lambda function for

24:02

your entire REST API, even if it is

24:04

200 endpoint. The whole app

24:07

there and then when a request comes in,

24:09

it routes to whatever part of your app

24:11

that you might need. There's a package called

24:13

Power Tools for Py, AWS Power Tools. I

24:15

don't know how to write Power Tools for

24:17

Py thought. Yeah,

24:19

I know the similar name. So they

24:22

have a really good like event resolver.

24:24

So you can actually, it almost

24:26

looks flask or some of

24:28

the other Python web frameworks.

24:30

And so you have this resolver,

24:33

whether it's API gateway, and AWS

24:36

or different, they have a few

24:38

different options for the API itself.

24:40

But yeah, you in theory, you

24:42

could have your entire API

24:45

behind a single Lambda function, but

24:47

then that's probably not optimal, right?

24:49

So you're, that's where you have

24:51

to figure out how to break that up. Yeah,

24:54

they do that same, the

24:56

decorators app.post or

24:58

yeah, and your endpoints

25:00

and you can do the, have variables

25:03

in there where maybe you have ID

25:05

as your lookup and it can slash

25:07

user slash ID is going to find

25:09

your, find a single user. In their

25:12

documentation, they actually address this a little

25:14

bit. Do you want to do, they

25:17

call it either like a micro

25:19

function pattern where maybe every single endpoint

25:21

has its own Lambda function. But yeah,

25:23

that's a lot of overhead to maintain.

25:25

If you had like you said, 200

25:27

endpoints, you have 200 Lambdas. You

25:30

got to upgrade them all at the same time.

25:32

So they have the right consistent data models and

25:35

all that. Yeah, that's gnarly. There's definitely some even

25:37

conflicting views on this. How micro

25:40

do you want to go? And

25:42

I was able to go to

25:44

AWS re-invent in November, and they

25:46

actually pitched this, this hybrid, maybe

25:48

if you take your like CRUD

25:51

operations, and maybe you have your

25:53

create, update, and delete all on

25:55

one Lambda that's with its configuration

25:57

for those, but your read is.

26:00

on another lambda. So maybe your

26:02

CRUD operations, they all interact with

26:04

a relational database, but your reader

26:06

just does reads from a Dynamo

26:09

database where you sync that data

26:11

up. And so you could have

26:13

your permissions separated for each of

26:15

those lambda functions and people

26:18

reading from an API don't always need the

26:20

same permissions as updating, deleting. And so yeah,

26:22

there's a lot of different ways to break

26:24

that up. And how micro do you go

26:26

with this? That's how they micro.

26:28

Can you go? Yeah. Yeah. Because it sounds

26:31

to me, like if you had many of them,

26:33

then all of a sudden you're back to, wait,

26:35

I did this because I didn't

26:37

want to be in DevOps. And now I'm a

26:39

different kind of DevOps. That package,

26:41

the Power Tools is does a lot

26:44

of like heavy lifting for you. At

26:46

PyCon, there is a talk on serverless

26:48

that they, the way they described the

26:50

Power Tools package was it, they had

26:53

said like codified your serverless best practices.

26:55

And it's really true. They give a

26:57

lot, there's so many different tools in

27:00

there. And there's a, a logger, like

27:02

a structured logger that works really well

27:04

with lamb. You don't even have to

27:06

use like the AWS login services. If

27:08

you want to use like Datadog or

27:11

Splunk or something else, you it's just a

27:13

structured logger and how you aggregate them is

27:15

like up to you. And you can even

27:17

customize how you format them, but it's works

27:20

really well with Lambda. Yeah. You probably

27:22

could actually capture exceptions and stuff with

27:25

something like Sentry even, right? Oh yeah. Python

27:27

code, there's no reason you couldn't. Some of

27:29

that comes into packaging up those libraries

27:31

for that. Yeah. You do have

27:33

to think of some of that

27:35

stuff, but Datadog, yeah,

27:38

Datadog, for instance, they provide something called like a

27:40

Lambda layer or a Lambda extension, which is another

27:42

way to package code up. That just makes it

27:45

a little bit easier. Yeah. There's a lot of

27:47

different ways to attack some of these problems. A

27:49

lot of that stuff, even though they have nice

27:51

libraries for them, it's really just calling a HTTP

27:53

endpoint and you could go, okay, we need something

27:56

really light. I don't know if request is already

27:58

included or it may be included. but there's

28:00

some gotta be some kind of HTTP thing

28:02

already included. We're just going to directly call

28:04

it, not pulling all these packages. Yeah. This

28:07

code looks nice. This power tools code, it

28:09

looks like well-written Python code.

28:11

They do some really amazing stuff

28:13

and they bring in a pedantic

28:16

too. Being mostly in serverless,

28:18

I've never really gotten to use like

28:20

fast API and leverage pedantic as much,

28:22

but with power tools you really can.

28:25

So, so they'll package up

28:27

pedantic for you. And so you can

28:29

actually, yeah, you can have

28:31

pedantic models for validation on these.

28:33

It's like a Lambda function, for

28:35

instance, it always receives an event.

28:37

There's always two arguments to the

28:39

fun, to the handler function. It's

28:41

event and context and like event

28:43

is always a, it's

28:46

a dictionary in Python. And so

28:48

they can always look different. If

28:51

you look in the power tools, GitHub,

28:53

their tests, they have an API, gateway

28:56

proxy event.json or whatever, right?

28:58

You don't want to parse that

29:00

out by yourself. They have

29:02

pedantic models or they might actually

29:05

just be Python data classes, but

29:07

that you can, you can say,

29:09

okay, yeah, this function is going to

29:11

be for an API gateway proxy event,

29:14

or it's going to be an S3

29:16

event or whatever it is. There's so many

29:18

different ways to receive events from

29:20

different AWS services. So yeah, power tools

29:23

gives you some nice validation and yeah,

29:25

you might just say, okay, yeah, the

29:27

body of this event, even though I

29:30

don't care about all this other stuff

29:32

that they include the path headers,

29:35

queer string parameters, but I just need like

29:37

the body of this. So you just say,

29:39

okay, event.body, and you can

29:41

even use, you can validate that further. The

29:43

event body is going to be a pedantic

29:46

model that you created. Yeah, there's a lot

29:48

of different pieces in here. If I was

29:50

working on this and it didn't already have

29:52

pedantic models, I would take this and go

29:54

to Jason pedantic. Oh, I didn't even know

29:56

this existed. That's very nice. Okay. Boom. Put

29:58

that right in there. And there

30:01

you go, it parses it onto a nested

30:04

tree, object tree of the model. But

30:06

if they already give it to you,

30:08

they already give it to you, then

30:10

just take what they give you. Yeah. Those

30:12

specific events might be data classes instead

30:14

of pedantic, just because you don't, that

30:16

way you don't have to package pedantic up

30:18

in your Lambda. But yeah, if you're already

30:21

figuring out a way to package power tools

30:23

here, or you're close enough that you probably

30:25

just include pedantic too. Yeah. I think they

30:27

just added this feature where it'll

30:29

actually generate open API schema for you.

30:31

Okay. Fast API does that as well,

30:33

right? That's something you can leverage power

30:36

tools to do now as well. Oh,

30:38

excellent. And then you can actually take

30:40

the open API schema and generate a

30:42

Python client for it on top of

30:44

that, I think with automation. So you

30:46

just say robots all the way down,

30:48

automated turtles all the way down. I

30:50

haven't used those open API generated clients

30:52

very much. I was always like

30:54

skeptical of them. I just feel heartless and

30:56

soulless, I guess, the word looks for. And

30:58

he's just like, okay, here's another star org,

31:01

star KW org thing. Or couldn't you

31:03

just write it, take some reasonable defaults

31:05

and give me some keyword. Or just

31:08

add to how I feel. But if it's better

31:10

than nothing, that's better than nothing. You can see

31:12

like power tools. They took a lot of influence

31:14

from fast API. It does seem

31:16

like it. Yeah, for sure. It's definitely really powerful.

31:18

And you get some of those same benefits. This

31:21

is new to me. It looks quite nice.

31:23

Another comment by chem is, I need to

31:25

use serverless functions for either things that run

31:27

briefly, like once a month on a schedule

31:30

or the code that processes stuff coming

31:32

in on an AWS SQS,

31:34

simple queuing service queue of

31:37

unknown schedule. So maybe

31:39

that's an interesting segue into how

31:42

do you call your serverless code? As

31:44

we touched on, there's a lot of

31:46

different ways from AWS, for instance, to

31:49

do as Yeah, like AWS Lambda has

31:51

Lambda function URLs. I haven't used those as

31:53

much, but if you just look at like

31:55

the different options and like power tools, for

31:58

instance, you can have a. load

32:00

balancer that's gonna, where you set

32:02

the endpoint to invoke a Lambda,

32:04

you can have API gateway, which

32:06

is another service they have. So

32:09

there's a lot of different ways,

32:11

yeah, SQS, that's almost getting into

32:13

a way of streaming or an

32:15

asynchronous way of processing data. Yeah,

32:18

maybe in AWS you're using

32:20

a queue, right? That's filling

32:22

up and you say, okay, yeah, every time this

32:24

queue is at this size or

32:26

this timeframe, invoke this Lambda and

32:28

process all these messages. So there's

32:30

a lot of different ways to

32:33

invoke a Lambda function. If it's, I mean,

32:35

really as simple as you can invoke them

32:38

like from the AWS CLI or, but yeah,

32:41

most people are probably have some kind of

32:43

API around it. Yeah, almost make them look

32:45

like just HTTP endpoints. Right. Markout

32:48

there says, not heard talk of

32:50

ECS, I don't think, but I've

32:52

been running web services using Fargate

32:54

serverless tasks on ECS for years

32:56

now. Are you

32:58

familiar with this? I haven't done anything with this. I'm like vaguely familiar with

33:00

it, but yeah, this is like a serverless,

33:03

yeah, serverless compute for

33:05

containers. I haven't used this personally,

33:08

but yeah, very like similar concept

33:10

where it scales up for you.

33:13

And yeah, you don't have to

33:15

have things running all the time, but yeah, it

33:17

can be dockerized applications. Now, in fact,

33:19

the company I work for now, they do

33:21

this with their Ruby on Rails applications. They

33:24

dockerize them and run with Fargate. This

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33:51

Docker containers of these things, the

33:53

less familiar you are with running that tech

33:55

stack, the better it is in Docker, you

33:57

know what I mean? Yeah, I could run

33:59

straight. Python. But if it's Ruby on Rails

34:01

or PHP, maybe it's going into a container. That

34:04

would make me feel a little bit better about

34:06

it. Especially if you're in that workflow of handing

34:08

something over to a DevOps team, right? Like you

34:11

can say, here's an image or a container

34:13

or a Dockerfile that, you know, that will

34:15

work for you. That's maybe a

34:17

little bit easier than trying

34:19

to explain how to set up an environment or

34:21

something. Yeah. Yeah. Fargate's a really good

34:24

serverless option too. Excellent. What

34:27

about performance? You talked about having a

34:29

whole API apps like FastAPI, Flask,

34:31

or whatever. The startup of those apps

34:34

can be somewhat, can be non-trivial basically.

34:36

And so then on the other side,

34:38

we've got databases and stuff. And one

34:40

of the bits of magic of databases

34:43

is the connection pooling that happens, right?

34:45

So the first connection might take 500

34:47

milliseconds, but the next one takes one.

34:50

It's already open effectively. That's

34:52

definitely something you really have to take into consideration

34:55

is like how much you can do. That's where

34:57

some of that like observability is some of like

34:59

the tracing that you can do and

35:01

profiling is really powerful. AWS

35:04

Lambda, for instance, they have

35:06

something called cold starts. The

35:09

first time like a Lambda gets

35:11

invoked, or maybe you have 10

35:14

Lambdas that get called at the same time,

35:16

that's going to invoke 10

35:18

separate Lambda functions. So that's like

35:20

great for the scale, right? That's

35:22

really nice. But on a cold

35:24

start, it's usually a little bit

35:26

slower invocation because it has to

35:28

initialize. I think what's happening behind

35:31

the scenes is they're like, they're moving

35:33

your code over that's going to get

35:35

executed. And anything that happens

35:37

like outside of your handler

35:39

functions, so importing libraries, sometimes

35:41

you're establishing a database

35:43

connection, maybe you're loading some

35:45

environment variables or some secrets.

35:48

And yeah, there's definitely performance

35:50

is something to consider. You

35:52

mentioned Rust. Yeah, there's probably

35:54

some more performant run times

35:56

for some of these serverless

35:58

functions. Yeah. and heard some

36:00

people say, okay, for

36:02

like client facing things, we're not going to

36:05

use serverless, like we just want that performance.

36:07

That can have an impact on you. Yeah,

36:09

on both ends that I've pointed out, right?

36:11

Like the App Store, but also the service,

36:14

the database stuff with the connection pooling and...

36:17

Yeah, relational databases too. That's an interesting

36:19

thing. Yeah, what do you guys do?

36:21

You mentioned Dynamo already. Yeah, so Dynamo

36:23

really performant for a lot

36:25

of connections, right? So Dynamo

36:28

is a serverless database that can

36:30

scale, you can query it over and that's not

36:32

going to... It doesn't reuse a

36:35

connection in the same way that like

36:37

a SQL database would. So that's an

36:39

excellent option. But if you

36:41

do have to connect to a relational database

36:43

and you have a lot of invocations, you

36:46

can use a like a proxy if

36:49

you're all in on AWS. And so

36:51

again, sorry if this is really AWS

36:53

heavy, but if you're using their like

36:55

relational database service RDS, you can use

36:58

RDS proxy, which will use like a

37:00

pool of connections for your Lambda function.

37:02

Oh, interesting. Yeah, that can give you

37:05

a lot of performance or at least

37:07

you won't be running out

37:09

of connections to your database. Another

37:11

thing too is just how you structure

37:14

that connection. I mentioned cold Lambdas, you

37:16

obviously have warm Lambdas too. A Lambda

37:18

has its handler function. And so anything

37:21

outside of the handler function can get

37:23

reused on a warm Lambda. So you

37:25

can establish the connection to a database

37:27

and it'll get reused on every invocation

37:30

that it can. That's cool. Do you have to

37:32

do anything explicit to make it do that or

37:34

is that just a... It just has to be

37:36

outside of that handler function, your

37:38

top level of your file. So it makes

37:40

me think almost one thing you would consider

37:43

is like profiling the import

37:45

statement almost right. And that's what

37:47

we normally do. But there's a

37:49

library called import profiler that actually

37:51

lets you time how long different

37:53

things take to import. It could

37:55

take a while, especially if you

37:57

come from not from a native.

38:00

of Python way of thinking in C

38:03

sharp or C plus or something

38:05

you say hash include or using

38:07

such and such. That's a compiler type thing

38:10

that really has no cost. But

38:12

there's code execution when you import something in

38:14

Python and some of these can take a

38:16

while right there's a lot of tools for

38:18

that there's some I think even maybe specific

38:20

for Lambda. I don't like data dog has

38:22

a profiler that gives you like this. I

38:25

forget what the graphic is called. And like a

38:27

flame graph. Yeah, that will give you like a

38:29

flame graph and show okay, yeah, it

38:31

took this long to make

38:33

your database connection this long to

38:35

import pedantic and it took this

38:37

long to make a call

38:40

to DynamoDB. So you actually break

38:42

that up. AWS has x ray,

38:44

I think which does something similar

38:46

to yeah, it's definitely something to

38:49

consider another just what your packaging

38:51

is definitely something to watch

38:53

for. And okay, yeah, I

38:55

mentioned using pants to package lambdas.

38:58

And they do. Hopefully

39:00

I don't butcher how this works behind

39:03

the scenes. But they're using rust and

39:05

they'll actually infer your dependencies for you.

39:07

And so they have a an integration

39:10

with AWS lambda. They also have it

39:12

for Google Cloud Function. So yeah, it'll

39:14

go through you say here's like my

39:16

AWS lambda function, this is the file

39:18

for it and the function that needs

39:20

to be called and it's going to

39:22

create a zip file for you that

39:25

has your lambda code in it and

39:27

it's going to find all those dependencies

39:29

you need. So it'll actually by default,

39:31

it's going to include Bodo that you

39:33

need if you're using Bodo, if you're

39:36

going to use pi MySQL

39:38

or whatever library it's going to

39:40

pull all those in and zip that

39:42

up for you. And so you just open

39:44

up that zip and you see, especially if

39:46

you're sharing code across your code base, maybe

39:49

you have a shared function to make some

39:51

of these database connections or calls like you

39:53

see everything that's going to go in there.

39:55

How like pants says

39:57

is it's file based, sometimes just for

40:00

like ease of imports, you might throw a

40:02

lot of stuff in like your, your init.py

40:04

file and say, okay, yeah, from, you add

40:06

all kind of bubble up all your things

40:09

that you want to import in there. If

40:11

one of those imports is also

40:13

using OpenCV and you don't need that, then pants

40:18

is going to say, Oh, he's importing

40:20

this. And because it's file-based now this

40:23

Lambda needs OpenCV, which is a massive

40:26

package that's going to, it's going

40:28

to impact your performance, especially in

40:30

those cold starts because that, that code

40:32

has to be moved over. So that's

40:34

pretty interesting. So an alternative

40:37

to saying, here's my requirements

40:39

or my pyproject.toml lock

40:41

file or whatever, that just lists everything the

40:43

entire program might use. This could say you

40:45

can import this function and to do that,

40:47

it imports these things, which import those things.

40:50

And then it just says, okay, that means

40:52

here's what you need. It's definitely one of

40:54

the best ways that I

40:56

found to, to package up Lambda functions.

40:59

I think some of the other tooling might do some

41:01

of this too, but yeah, a lot

41:03

of times it would require like requirements

41:05

that TXT, but if you have a

41:07

large code base too, where maybe you

41:09

do have this shared module for that,

41:12

maybe you have 30 different Lambda functions

41:14

that are all going to use some kind

41:16

of helper function. It's just going to

41:18

go and grab that. And it doesn't have to be

41:20

like pip installable pants is smart enough to just be

41:22

like, okay, it needs this code. But

41:24

yeah, be careful how there's so many other

41:27

cool things that pants is doing that they

41:29

have some really nice stuff for testing

41:31

and the linting and formatting. That's there's

41:33

a lot of really good stuff

41:35

that they're doing. So yeah, I had Benji on

41:37

the show to talk about pants. That was fun.

41:39

Yeah. Let me go back to this picture. It's

41:41

just the picture. I have a lot of things

41:43

open in my screen now there.

41:47

So on my server setup that I described,

41:49

which is a bunch of Docker containers running

41:51

on one big machine, I can

41:53

go in there and I can say tail this

41:56

log and see all the traffic to all the

41:58

different containers. I can tell another log. You

42:00

can just see like the login,

42:02

logbook, logguru, whatever output of that

42:04

or just web track. There's

42:07

different ways to just go, I'm just going

42:09

to sit back and look at it for

42:11

a minute. Make sure it's chilling, right? If

42:13

everything's so transient, not so easy in the

42:16

same way. So what do you do? Power

42:18

Tools does, they have their structured logger that

42:20

helps a lot. But yeah, you have to

42:22

aggregate these logs somewhere because yeah, you can't,

42:24

a Lambda function you can't like SSH into.

42:27

Yeah, you can't have to thank you long. Yeah,

42:30

yeah. You need to have

42:33

some way to aggregate these. So like AWS

42:35

has CloudWatch where that will

42:37

like by default kind of log all

42:39

of your standard out. So

42:41

even like a print statement would

42:43

go to CloudWatch just by default.

42:45

But you probably want to structure

42:47

these better with most likely in

42:49

JSON format, just most tooling around

42:51

those is going to help you.

42:53

Yeah, the Power Tools structured logger

42:56

is really good. And you can

42:58

have a single log statement, but

43:00

you can append different keys to it. And

43:02

it's pretty powerful, especially because you don't want

43:04

to like, I think, so if

43:06

you just printed something in a Lambda function,

43:09

for instance, that's going to be like a

43:11

different row on each of your, by default

43:14

CloudWatch, it'll be, how

43:16

it breaks it up is really odd unless you

43:19

have some kind of structure to them. Okay. So

43:21

definitely something to consider. Something

43:24

else you can do is, yeah, there's

43:26

metrics you can do. So

43:28

like how it works with like CloudWatch,

43:30

they have a specific format. And if

43:32

you use that format, you can, it'll

43:35

automatically pull that in as a metric.

43:37

And like Datadog has something similar where

43:39

you can actually go in there. You

43:41

can look at your logs and say, find a value

43:44

and be like, I want this to be a metric

43:46

now. And so that's

43:48

really powerful. The metrics

43:50

sounds cool. So I see logging and

43:52

racing. What's the difference between those

43:54

things? Tracing is a level and

43:56

just a high level of logging. Tracing

43:58

does have a lot more to do. with your

44:00

performance or maybe even closer

44:02

to tracking some of these

44:05

metrics, right? I've used the

44:07

Datadog tracer a lot and

44:09

I've used the AWS like x-ray, their

44:11

tracing utility a little bit too and

44:13

so like those will show you. So

44:15

like maybe you are reaching out to

44:17

a database right into S3. Almost

44:20

like a APM application performance monitoring

44:22

where it says, yes, you spent

44:24

this much time in a SQL

44:26

query and this much time in

44:30

idantic serialization whereas the login would say a

44:32

user has been sent a message. Yep, tracing

44:34

definitely is probably more around your performance and

44:36

things like that. So I've been saying that

44:39

I could do that. You see it in

44:41

the Django debug tool or in the pyramid

44:43

debug tool bar but they'll be like, here's

44:45

your code and here's all your SQL queries

44:47

and here's how long they talk and you're

44:50

like, wow, that thing is reaching deep down

44:52

in there. The Datadog one is very interesting

44:54

because it just knows that this

44:56

is a SQL connection and it tells you

44:58

like, oh, okay, this SQL connection took this

45:01

long and it's like I didn't tell it to

45:03

even trace that. Like it just like it knows

45:05

really well. Yeah, so like the instrumentation. So one

45:07

thing to know a SQL connection to open is

45:10

another to say and here's what it sent over

45:12

SSL by the way. How do you get in

45:14

there? Yeah, yeah, it's definitely. It's

45:16

in process so it can do a lot

45:18

but it is impressive to see those things

45:20

that work. Alright, so that's probably what the

45:22

tracing is about, right? Definitely probably more around

45:24

performance. You can put some different things in

45:26

tracing too. I've used it to say, we

45:29

talked about those like database connections to say,

45:31

oh yeah, this is reusing a connection

45:33

here because I was trying to like debug some stuff

45:35

on am I creating a

45:37

connection too many times so I don't

45:39

want to be so yeah, you can

45:41

put some other useful things in tracing as

45:43

well. Yeah, and pad out in the

45:45

audience. Let's move around. We're using many

45:47

micro services like single execution involves many

45:49

services basically. It's hard to follow the

45:52

logs between the services and tracing helps

45:54

tie that together. Yeah, that's for sure.

45:56

Alright, let's close this out Tony with

45:58

one more thing that I'm not sure. sure

46:00

how constructive it can be. There probably

46:02

is some ways, but testing,

46:04

right? Yeah. You

46:07

can't set up your own, if you could set

46:09

up your own Lambda cluster, you might just

46:11

run that for yourself, right? How are you

46:13

going to do this, right? Yeah, to some

46:15

extent you can, right? There's a Lambda Docker

46:17

image that you could run locally and

46:20

you can do that. But if your

46:22

Lambda is reaching out to DynamoDB, I

46:24

guess there's technically a DynamoDB container as

46:27

well. It's a

46:29

lot of overhead to set this up, but rather

46:31

than just doing flask start or whatever, the

46:33

command is still going up a flask. I

46:35

press the go button in my IDE and

46:38

now it's... That's

46:40

definitely... And there's more and more tooling coming

46:42

out that's coming out before this kind of

46:45

stuff. But if you can unit test,

46:47

there's no reason you can't just run

46:50

unit tests locally. But

46:52

when you start getting into the integration test,

46:55

you probably get to the point where maybe

46:58

you just deploy to

47:00

actual services and it's always trade-offs,

47:02

right? There's costs associated with it.

47:04

There's the overhead of, okay, how

47:06

can I deploy to an

47:09

isolated environment? But maybe it interacts

47:11

with another microservice. There's definitely trade-offs,

47:13

but testing is... I could see

47:15

that you might come up with

47:17

a QA environment almost, like a

47:19

mirror image that doesn't share any

47:21

data but is sufficiently close. But

47:23

then you're running... I mean, that's

47:25

a pretty big commitment because you're

47:27

running a whole replica of whatever

47:29

you have. Yeah, QA environments are

47:31

great, but you might even want

47:33

lower than QA. You might want

47:35

to have a dev or like

47:37

a one place I worked at,

47:39

we would spin up an entire

47:41

environment for every PR. You

47:44

could actually... Like when you created a

47:46

PR, that environment got spun up and

47:48

it ran your integration tests and system

47:50

tests against that environment, which

47:52

simulated your prod environment a little

47:54

bit better than running locally

47:56

on your machine. Certainly a challenge

47:59

to test. this and yeah I can

48:01

imagine that it is yeah and there's always this

48:03

one-off things too right you can't

48:06

really simulate like that memory limitation

48:08

of a lambda locally as

48:10

much as when you deploy it and things

48:12

like that so that would be much much

48:14

harder I maybe you could run a docker

48:17

container and put a memory limit on it

48:19

that might work but it's you're back into

48:21

like more and more DevOps to avoid DevOps

48:24

so there it goes but interesting all right

48:26

anything else you want to add to this

48:28

conversation before we wrap it up about out

48:31

of time here I'm done I have it

48:33

hopefully we covered enough yeah there's a lot

48:35

of good resources the tooling that I've mentioned

48:37

like power tools and pants just

48:39

amazing communities like power tools has a

48:42

discord and go on there and ask

48:44

for help and they're super helpful pants

48:46

has a slack channel you can join

48:48

their slack and ask about things and

48:50

so those two communities have been really

48:52

good and really helpful in this a

48:54

lot of good talks that are available on

48:57

YouTube too so just yeah there's definitely resources

48:59

out there and a lot of people have

49:01

bought this for a while so excellent you

49:03

know just start from just create function and

49:06

start typing yeah cool all right before you

49:08

get out of here though let's get your

49:10

recommendation for a PyPI package something

49:13

notable something fun we've talked a lot about

49:16

it but power tools it's definitely one that

49:18

is like every day getting used for me

49:20

so that yeah power tools

49:22

for lambda and Python they actually support

49:25

other languages too so

49:27

they have the same functionality

49:29

for node.js for typescript and

49:31

.net and so sure this

49:33

one definitely leveraging power tools

49:35

and pydantic together just really

49:38

made like a serverless a lot of fun

49:41

to write yeah definitely doing great things there

49:43

excellent I'll put all those things in the

49:45

show notes yeah it's been great to talk

49:47

to you thanks for sharing your journey down

49:50

the serverless path yeah

49:52

thanks for having me yeah joychattin

49:54

say bye yeah this has been

49:56

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