Advanced SQL for Query Tuning and Performance Optimization

Course details

SQL queries can be fast and highly efficient, but they can also be slow and demand excessive CPU and memory resources. For many SQL programmers, occasional bouts with long-running queries and poor performance are simply par for the course. But by gaining a better understanding of how databases translate SQL queries into execution plans, you can take steps to avoid these issues. In this course, Dan Sullivan shows you how to analyze query execution plans and use data modeling strategies to boost query performance. Dan describes how SQL queries are executed, highlights different types of indexes and how they factor in query tuning, covers several methods for performing joins, and discusses how to use partitioning and materialized views to improve performance. Plus, Dan shows you how to run PostgreSQL in GitHub Codespaces so you can get started learning faster.

Concepts

0. Introduction

01 - Reduce query response time with query tuning
02 - What you should know

1. How SQL Executes a Query

03 - From declarative SQL to a procedural execution plan
04 - Scanning tables and indexes
05 - Joining tables
06 - Partitioning data
07 - Challenge - Choosing how to partition a table
08 - Solution - Choosing how to partition a table

2. PostgreSQL Tools for Tuning

09 - Using PostgreSQL in Codespaces
10 - Explain and analyze
11 - Example plan - Selecting with a WHERE clause
12 - Indexes
13 - Challenge - Generating a query execution plan
14 - Solution - Generating a query execution plan

3. Types of Indexes

15 - Indexing
16 - B-tree indexes
17 - B-tree index example plan
18 - Bitmap indexes
19 - Bitmap index example plan
20 - Hash indexes
21 - Hash index example plan
22 - Bloom filter indexes
23 - PostgreSQL-specific indexes
24 - Challenge - Choosing an index
25 - Solution - Choosing an index

4. Tuning Joins

26 - Types of joins
27 - Nested loops
28 - Nested loop example plan
29 - Hash joins
30 - Hash join example plan
31 - Merge joins
32 - Merge join example
33 - Subqueries vs. joins
34 - Challenge - Designing a join
35 - Solution - Designing a join

5. Partitioning Data

36 - Horizontal vs. vertical partitioning
37 - Partition by range
38 - Partition by range example
39 - Partition by list
40 - Partition by list example
41 - Partition by hash
42 - Partition by hash example
43 - Challenge - Partitioning a table
44 - Solution - Partitioning a table

6. Materialized Views

45 - Materialized views
46 - Creating materialized views
47 - Refreshing materialized views
48 - Challenge - Creating a materialized view
49 - Solution - Creating a materialized view

7. Database Statistics and Identifying Slow Queries

50 - Collect statistics about data in tables
51 - Analyzing execution statistics with pg stat statements
52 - Reviewing execution plans with the auto explain module
53 - Additional analysis with other pg stats data
54 - Challenge - Analyze schema statistics
55 - Solution - Analyze schema statistics

8. Other Optimization Techniques

56 - Using common table expressions to avoid repetitive computation
57 - Hints to the Query Optimizer
58 - Parallel query execution
59 - Improving cache utilization
60 - Miscellaneous tips
61 - Challenge - Design a common table expression
62 - Solution - Design a common table expression

Conclusion

63 - Next steps