English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 2h 07m | 248 MB
Many applications require a relational database. But poorly designed data architecture and poorly written SQL can result in subpar performance, unreliable services, and difficulty scaling. This course includes hands-on examples and lessons that show how to build scalable and resilient databases to support any application. Learn how to write optimized SQL for transaction processing, use indexes to reduce read latency, partition data to improve scalability, and use established design patterns. Instructor Dan Sullivan also explores object relational mapping and shows how to respond to database errors such as query timeouts and refused connections. After completing this course, you will be able to design robust database applications that can scale to meet increasingly demanding workloads.
Topics include:
- Creating tables and loading data
- Parameterizing SELECT statements
- Indexing tables
- Object relational mapping
- Partitioning
- Error trapping
- Monitoring and logging
- Automated schema migration
Table of Contents
1 Getting started with SQL for application developers
2 Designing an ecommerce application database
3 Creating tables and loading data
4 Using environment variables for connection parameters
5 Connecting to a database
6 Parameterizing SELECT statements
7 Avoiding N+1 queries
8 Challenge Write a parameterized query in Python
9 Solution How to write a parameterized query in Python
10 Overview of index types
11 Using B-tree indexes and range scans
12 Using hash indexes and equality operations
13 Covering indexes
14 Indexes and bulk data loading
15 Avoiding index locks
16 Challenge Define a B-tree and a hash index
17 Solution Defining B-tree and hash indexes
18 Overview of object-relational mapping
19 Introduction to SQLAlchemy
20 Installing SQLAlchemy
21 Query with SQLAlchemy
22 Limitations of ORM and cautionary advice
23 Challenge Create an ORM model
24 Solution Create an ORM model
25 Overview of partitioning
26 Range partitioning
27 List partitioning
28 Hash partitioning
29 Querying partitioned tables
30 Using read-only replicas
31 Challenge Define a partitioned table
32 Solution Defining a partitioned table
33 Database errors
34 Error trapping
35 Database not responding, timeouts, and exponential backoff
36 Connection pooling and bulk heads
37 Monitoring and logging best practices
38 Challenge When your Python application fails
39 Solution When your Python application fails
40 Understanding schema changes
41 Ad hoc scripts for schema changes
42 Automated schema migration with Flyway
43 Automated schema migration with Liquibase
44 Challenge
45 Solution
46 Key considerations
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