The podcast discusses the importance of row estimates in Postgres query planner. Topics include join strategies, auto vacuum, optimizing row estimates and statistics target, the problem of row estimates, and materialized views, indexes, and hints.
Accurate row estimates are crucial for optimizing query performance and avoiding performance issues related to nested loops.
The analyze command plays a vital role in gathering statistics to improve query plans and comparing estimated and actual number of rows returned by the query plan.
Deep dives
Row Estimates and Query Planner
The podcast episode discusses the importance of accurate row estimates in the query planner. It explains that many performance issues related to nested loops can be attributed to bad row estimates and poor planner choices. The episode dives into how Postgres makes these decisions and offers suggestions for users to improve statistics and guide the planner. It highlights the trade-offs of nested loops and explains that other join algorithms like hash joins and merge joins may be more efficient in certain scenarios. Overall, the episode emphasizes the significance of accurate row estimates in optimizing query performance.
Analyzing Tables and Recollecting Statistics
The podcast episode highlights the role of the analyze command in gathering statistics to improve query plans. It discusses the importance of running explain analyze to compare estimated and actual number of rows returned by the query plan. The episode suggests that if issues are identified, users can analyze individual tables or columns to recollect statistics. It also mentions the option to set the analyze scale factor and the role of auto vacuum in automatically analyzing tables. It stresses the need to ensure that statistics are up to date, especially during major version upgrades.
Correlation and Extended Statistics
The podcast episode explores the challenges posed by correlated columns in query planning and the use of extended statistics to address these issues. It discusses that highly correlated columns can lead to incorrect row estimates and suboptimal plans. The episode mentions the option to educate the planner about the dependencies between columns using extended statistics. It also touches upon the use of materialized views and foreign data wrapper tables for creating statistics. Additionally, the episode mentions the possibility of using hints, although it acknowledges the divided opinions on their usage.