floy: Introduction to Postgres Query Planning

Get an in-depth look at PostgreSQL query planning, exploring the complexity of query planning, the role of the planner, plan nodes, and optimizing performance.

Key takeaways

Query planning in PostgreSQL is complex and influenced by many factors, including the complexity of the query, the size and distribution of the data, and the configuration of the database.
The query planner tries to choose the plan that will result in the fastest execution time, but it’s not always successful and human intervention may be necessary.
The EXPLAIN command is used to see the query plan chosen by the planner, which can be helpful in understanding why a particular plan was chosen.
The planner is forced to choose between sequential scans and index scans, each with its own strengths and weaknesses.
The planner also has to consider the impact of page turns on performance, and will choose plans that minimize page turns.
Index scans can be faster than sequential scans in many cases, but may not always be the best choice.
The planner must also consider the cost of reading pages from disk, which can be influenced by factors such as the distribution of values in the table.
The planner is influenced by various plan nodes, including Seq Scan, Index Scan, Index Only Scan, Hash, Merge, and others.
Plan nodes can have various attributes, such as cost, perial, and nodename.
The planner can be influenced by various statistics, such as the number of unique values in a column.
The planner can also be influenced by various settings, such as random_page_cost and seq_page_cost.
It’s important to consider the distribution of values in the table, as this can affect the choice of plan node.
The planner can also be influenced by the presence of indexes, and will choose plans that make use of indexes.
The planner must also consider the impact of joins on performance, and will choose plans that minimize the number of joins.
The planner can be influenced by various join orders, and will choose the order that results in the fastest execution time.
The planner can also be influenced by the use of subqueries, and will choose plans that minimize the number of subqueries.
The planner is influenced by various limiting factors, such as the limit clause, which can affect the choice of plan node.
It’s important to consider the impact of caching on performance, and to use VACUUM and ANALYZE to maintain the efficiency of the database.
The planner is influenced by various statistics, such as the number of rows in a table, and will choose plans that minimize the number of rows that need to be processed.
The planner can be influenced by various factors, such as the distribution of values in a column, and will choose plans that result in the fastest execution time.
It’s important to consider the impact of data distribution on performance, and to use ANALYZE to maintain the efficiency of the database.
The planner is influenced by various settings, such as random_page_cost and seq_page_cost, and will choose plans that minimize the number of page turns.
It’s important to consider the impact of page turns on performance, and to use VACUUM and ANALYZE to maintain the efficiency of the database.
The planner is influenced by various calculating factors, such as the cost of reading pages from disk, and will choose plans that minimize the number of page turns.

floy: Introduction to Postgres Query Planning

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