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Advanced SQL for Query Tuning and Performance Optimization

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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.


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

  • 02 - 1. How SQL Executes a Query
  • 01 - From declarative SQL to a procedural execution plan
  • 02 - Scanning tables and indexes
  • 03 - Joining tables
  • 04 - Partitioning data
  • 05 - Challenge Choosing how to partition a table
  • 06 - Solution Choosing how to partition a table

  • 03 - 2. PostgreSQL Tools for Tuning
  • 01 - Using PostgreSQL in Codespaces
  • 02 - Explain and analyze
  • 03 - Example plan Selecting with a WHERE clause
  • 04 - Indexes
  • 05 - Challenge Generating a query execution plan
  • 06 - Solution Generating a query execution plan

  • 04 - 3. Types of Indexes
  • 01 - Indexing
  • 02 - B-tree indexes
  • 03 - B-tree index example plan
  • 04 - Bitmap indexes
  • 05 - Bitmap index example plan
  • 06 - Hash indexes
  • 07 - Hash index example plan
  • 08 - Bloom filter indexes
  • 09 - PostgreSQL-specific indexes
  • 10 - Challenge Choosing an index
  • 11 - Solution Choosing an index

  • 05 - 4. Tuning Joins
  • 01 - Types of joins
  • 02 - Nested loops
  • 03 - Nested loop example plan
  • 04 - Hash joins
  • 05 - Hash join example plan
  • 06 - Merge joins
  • 07 - Merge join example
  • 08 - Subqueries vs. joins
  • 09 - Challenge Designing a join
  • 10 - Solution Designing a join

  • 06 - 5. Partitioning Data
  • 01 - Horizontal vs. vertical partitioning
  • 02 - Partition by range
  • 03 - Partition by range example
  • 04 - Partition by list
  • 05 - Partition by list example
  • 06 - Partition by hash
  • 07 - Partition by hash example
  • 08 - Challenge Partitioning a table
  • 09 - Solution Partitioning a table

  • 07 - 6. Materialized Views
  • 01 - Materialized views
  • 02 - Creating materialized views
  • 03 - Refreshing materialized views
  • 04 - Challenge Creating a materialized view
  • 05 - Solution Creating a materialized view

  • 08 - 7. Database Statistics and Identifying Slow Queries
  • 01 - Collect statistics about data in tables
  • 02 - Analyzing execution statistics with pg stat statements
  • 03 - Reviewing execution plans with the auto explain module
  • 04 - Additional analysis with other pg stats data
  • 05 - Challenge Analyze schema statistics
  • 06 - Solution Analyze schema statistics

  • 09 - 8. Other Optimization Techniques
  • 01 - Using common table expressions to avoid repetitive computation
  • 02 - Hints to the Query Optimizer
  • 03 - Parallel query execution
  • 04 - Improving cache utilization
  • 05 - Miscellaneous tips
  • 06 - Challenge Design a common table expression
  • 07 - Solution Design a common table expression

  • 10 - Conclusion
  • 01 - Next steps
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