MongoDB with Async Python Transcripts
Chapter: Welcome to the Course
Lecture: Course Topics
Login or
purchase this course
to watch this video and the rest of the course contents.
0:00
What are we going to cover in this course? Well, we're going to have an introductory chapter that tells you about everything like this.
0:07
You are here. You know what that's about. We're going to talk about what are document databases and how do they work in general,
0:14
as well as a technical overview of some of the MongoDB internals, how it works,
0:19
how does it allow you to query things that are nested deep within some kind of document, as we just saw. We're going to do a few foundational topics.
0:29
And we saw these important building blocks, asyncio, Pydantic and so on. So we're going to have a few chapters that make sure you
0:36
really understand those super well, because they're an important part to making all of this fit together. First one of those is Pydantic.
0:45
Then we're going to do asyncio as well. Then we'll take a couple passes about building with and learning about Beanie.
0:53
We're going to do a Beanie quick start. Like what is the simple, minimal application and that we can get set up to just see all the moving parts
1:01
and how it works. And then we're gonna look at modeling a cool, well-known place, well-known dataset with documents
1:11
or how that might look and what is the difference of modeling if you have a relational database and you're aiming for third normal form
1:18
and foreign key constraints and all of that? Well, how does that change in a document database? We'll talk about that.
1:24
Speaking of a realistic data source, We're going to model the PyPI data, so pypi.org with packages and releases and maintainers and users, all of that.
1:36
We're gonna model that with Beanie and Pydantic classes. And then we're gonna take that classes and the Beanie interaction that we've already created
1:45
with the queries and collections and all that. And we're gonna plug that into FastAPI and give us a really cool example of,
1:54
Well, here's an end to end API talking about this pi pi data that we've modeled, but also
2:00
how does it fit into a web framework, especially one that's pedantic friendly, let's say, when
2:07
I told you that MongoDB was fast, there was a couple of techniques that I had in mind, because it's not always fast out of the box.
2:14
If you just throw a bunch of data in it, and you start doing queries, you might be really
2:18
disappointed, could be really, really slow for a whole bunch of reasons.
2:22
So we're going to look at all the ways and knobs that we can turn, as well as programming
2:27
techniques we can apply to make MongoDB go from who knows, maybe a one second response
2:33
time to a 10 millisecond response time, you know, 100 times faster, maybe even 1000, who knows.
2:40
And finally, we'll talk a little bit about how do you deploy and host MongoDB.
2:46
There are database as a service or MongoDB as a service places that you can use.
2:50
And if you want to use them, great, you're kind of don't need this chapter.
2:54
But many people don't want to put their data in the cloud or for various operational reasons
2:59
or even pricing reasons, you might decide to deploy it differently.
3:04
So if you want to self host MongoDB, either internally or in the cloud next year apps, we'll see how to do that.
3:10
And finally, in the performance section, we made our code faster, kind of in isolation, It would be cool to see, well, how fast is our API overall?
3:22
So we're going to do some cool load testing on not just MongoDB itself or Beanie queries
3:29
themselves, but the entire system that we built to wrap up the class using a really cool load testing framework called Locust. That's it.
3:38
I'm really excited about these topics I put together for you. I think it's going to be a really fun sequence and you're going to learn a ton.