Explaining EXPLAIN: A dive into PostgreSQL's EXPLAIN plans with Richard Yen - DjangoCon US 2022

Explaining EXPLAIN

• EXPLAIN is used to understand the query planner’s thought process, but it does not provide information on why the planner chose a particular execution plan.
• The query planner uses a cost-based approach, estimating the cost of each possible execution plan and choosing the lowest-cost plan.
• Analyses and vacuum are used to refresh statistics in the database and remove dead tuples, respectively.

Query Planner’s Thought Process

• The query planner considers the tables, columns, and indexes involved in a query, evaluating the most efficient way to obtain the desired data.
• The planner considers the cost of various execution plans, including sequential scans, hash joins, and index scans.

Stats and Statistics

• Statistics are used to estimate the number of rows in a table, the selectivity of a query, and the distribution of values in a column.
• The PostgreSQL engine uses statistics to inform its query planning decisions.

Index Scans and Index-Only Scans

• Index scans and index-only scans can be highly efficient, but they may not always be used by the query planner.
• Index scans are used when the query planner determines that an index can be used to speed up query execution.
• Index-only scans are used when the query planner determines that the desired data is present in an index.

Auto-Explain

• Auto-explain is an extension that forces the query planner to display the query plan, including statistics and costs.
• Auto-explain can be used to understand the query planner’s thought process and identify performance bottlenecks.

Log Statement

• The log statement prints the query that is issued before it runs, along with the start time and processing time.
• The log statement can be used to track query performance and identify slow queries.

Example Scenarios

• Example scenarios illustrate the importance of indexing, query planning, and query optimization.
• Examples include using the EXPLAIN command to understand the query planner’s thought process, identifying performance bottlenecks, and optimizing query execution.

Real-World Examples

• Real-world examples illustrate the importance of query planning and optimization in production environments.
• Examples include using the EXPLAIN command to troubleshoot slow queries, and optimizing query execution for improved performance.