MongoDB with Async Python

7h 19m 54s
English
Paid

Course description

This course will teach you how to use MongoDB and document databases to build simpler and faster data-driven applications.

We start by explaining the origin and major concepts of NoSQL and document databases. You then learn how to work with MongoDB from its native shell as well as many of the CLI and GUI management tools.

Many MongoDB courses stop there. This course is meant to be a practical end-to-end coverage of MongoDB. We go beyond scratching the surface by covering real-world topics.

You'll see how to use Beanie (a popular ODM for MongoDB - think ORM for NoSQL) to map classes to MongoDB. Beanie is based on state-of-the-art Python technologies such as Pydantic and Python's async and await.

In this code-based and hands-on, demo-driven course, we will build some simple example apps using Beanie. Then we'll move on to modeling real PyPI data with 100,000s of records in MongoDB. Once we have our Python code working with the PyPI data, we'll build out a full FastAPI API around the data showing the smooth integration of Beanie and async MongoDB within FastAPI.

After we master working with MongoDB from Python, we'll turn our attention to performance. We take a large database with millions of data points and make it run hundreds of times faster than you get out-of-the-box with MongoDB. We test our performance changes with both custom Python code and the Locust load testing framework.

We wrap up the course by deploying MongoDB to production Linux servers. There are a few very important steps to getting MongoDB running in production and we'll go step-by-step through this setup.

In the end, you'll be ready to start building and running high-performance, MongoDB-backed, data-driven applications.

Read more about the course

In this course, you will:

  • How document databases, such as MongoDB, work
  • Where MongoDB fits in the larger scope of databases used in the world
  • How to install and configure MongoDB and several management tools and GUIs
  • A basic set of MongoDB's native shell commands and queries
  • Foundational technologies such as Pydantic and Python's async and await
  • How to design data models with Beanie and Pydantic
  • Understand the tradeoffs when modeling data with documents
  • Learn when it's a good idea (and when it's a bad one) to embed data within other records
  • Use ORM-style programming with MongoDB and Beanie
  • Use more efficient "in-place" operations such as addToSet with Beanie
  • Design projection classes in Pydantic for improved performance
  • How to safely store user accounts (namely passwords) in MongoDB
  • To deeply integrate Beanie and MongoDB with FastAPI
  • Create complex indexes in MongoDB from Beanie for 1000x performance boosts
  • Use indexes to enforce data integrity in MongoDB
  • Safely deploy MongoDB in a self-hosted environment within a cloud provider on multiple Linux machines
  • Use the load testing framework Locust to probe and test the performance limits of your MongoDB-based web APIs
  • And lots more

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#1: Introducing the Course

All Course Lessons (111)

#Lesson TitleDurationAccess
1
Introducing the Course Demo
00:54
2
MongoDB is Loved
03:06
3
MongoDB is fast
03:44
4
Python's asyncio
02:41
5
What is Beanie and What is an ODM?
04:28
6
Course Topics
03:44
7
Meet Your Instructor: Michael Kennedy
01:15
8
Setup Introduction
02:23
9
Installing MongoDB
02:14
10
Studio 3T GUI
01:16
11
Importing PyPI Data
02:35
12
Verifying the Data with Studio 3T
02:30
13
Document Dbs introduction
01:04
14
How Document Databases Work
03:27
15
Who uses MongoDB?
03:01
16
Mongo Shell
02:13
17
A More Complex Query
01:34
18
And Operators in Queries
01:09
19
Querying Embedded Data
01:41
20
Studio 3T: A Better Shell
02:04
21
Query Operators
02:55
22
Queries: Logical Operators
00:50
23
Queries: Projections
04:24
24
Pydantic Introduction
00:49
25
A Time of Change for Pydantic
02:12
26
Get the Plugins
01:03
27
Built on Pydantic
04:01
28
Project Setup
05:11
29
Our First Pydantic Model
06:20
30
JSON to Pydantic Converter
05:36
31
Get the Full Story on Pydantic
00:56
32
Pydantic has a Solid Foundation
01:19
33
Async introduction
01:24
34
Async for Speed
04:07
35
Async for Scalability
01:02
36
Synchronous Execution Example
04:02
37
Asynchronous Execution Example
02:21
38
Skeleton async Program
08:27
39
Full Concurrency Weather Client
07:49
40
Beanie Quickstart Intro
00:45
41
Motor, MongoDB's Async Driver
01:49
42
Beanie Quickstart: Part 1 Classes
12:23
43
Beanie Quickstart: Part 2 Connections
10:43
44
Beanie Quickstart: Part 3 Inserts
04:02
45
Beanie Quickstart: Part 4 Queries
08:43
46
Beanie Quickstart: part 5 settings
05:29
47
Lightning Review of Beanie
03:13
48
Get the Full Story of Beanie
00:41
49
Bunnet, the Synchronous Beanie
01:16
50
Modeling Introduction
00:58
51
Modeling: Relational vs. Documents
03:28
52
To Embed or not to Embed?
05:12
53
What is an Integration Database?
03:12
54
Looking for More Guidance?
00:40
55
PyPI Api Introduction
00:47
56
Recall: Importing the Data
01:03
57
Review: The Data Model
05:15
58
Creating the DB Models
08:35
59
Configuring Collections from Beanie
02:47
60
Connecting to MongoDB
04:52
61
CLI Skeleton
04:39
62
ClI REPL
01:40
63
Summary Stats
05:30
64
Recent Packages
05:05
65
Finding Packages and Releases
10:14
66
Creating a Release
07:01
67
Concurrency Safe Create Release
11:03
68
Creating Users
05:27
69
Setting the User's Password
06:45
70
FastAPI Introduction
01:24
71
FastAPI Skeleton App
06:10
72
API Endpoints Ready
08:11
73
Package Details Implementation
12:07
74
Sometimes API focused models are required
08:36
75
Stats API
03:36
76
The Home Page Doesn't Belong in the API Docs
02:01
77
Static Files: CSS
10:42
78
Introduction to DB Performance
00:48
79
Levers and Knobs of MongoDB Performance
04:38
80
Creating Indexes in the Shell
02:22
81
Our First Python to MongoDB Index
08:49
82
Releases Counted in 1ms
06:40
83
Uniqueness Index for Users
03:48
84
Projections for Packages
09:08
85
Concept: Projections in Beanie
01:11
86
Document Design from a Performance Perspective
03:52
87
Hosting Introduction
01:14
88
Don't Do What These Companies Did with MongoDB
02:09
89
MongoDB as a Service Options
02:05
90
MongoDB's Security Checklist
02:12
91
Getting the Server Ready
03:13
92
Limit Network Access with VPCs and Firewall
05:36
93
Creating an Encryption Key
01:18
94
Installing MongoDB
03:45
95
Configuring MongoDB, Part 1
04:40
96
Adding Authentication to MongoDB
04:40
97
Connecting to Production MongoDB with Python
05:44
98
Importing Data Introduction for Production MongoDB
03:06
99
Connecting to a Remote MongoDB
06:25
100
Testing our Python Code in Production
06:26
101
Final Comments on Speed
01:51
102
Load Testing Introduction
01:08
103
Introducing Locust
04:21
104
Consider a Real Server Topology
01:13
105
Running Locust for Max RPS
10:57
106
Running Locust for Max Users
09:05
107
Faster Locust Tests
01:10
108
Finish Line!
00:42
109
Git the Source Code, Again
00:30
110
Final Review
06:11
111
Stay in Touch
01:02

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