MongoDB with Async Python

7h 19m 54s
English
Paid
April 26, 2024

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.

More

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

Watch Online MongoDB with Async Python

Join premium to watch
Go to premium
# Title Duration
1 Introducing the Course 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

Similar courses to MongoDB with Async Python

Data Science Jumpstart with 10 Projects Course

Data Science Jumpstart with 10 Projects CourseTalkpython

Duration 3 hours 12 minutes 21 seconds
Python and Django Full Stack Web Developer Bootcamp

Python and Django Full Stack Web Developer Bootcampudemy

Duration 31 hours 54 minutes 39 seconds
PyTorch for Deep Learning with Python Bootcamp

PyTorch for Deep Learning with Python Bootcampudemy

Duration 17 hours 2 minutes 14 seconds