How Can We Optimize Groupwise Maximum Queries in Postgres to Avoid Excessive Table Scans?

Optimizing Groupwise Maximum Queries

The query in question aims to retrieve the rows with the maximum id value for each unique option_id in the records table. However, the current implementation exhibits inefficiency due to excessive table scans.

Why the Current Query is Inefficient

The issue lies in the nested loop join used to identify rows with maximum id values. This join requires Postgres to scan the entire records table several times, leading to high execution time and resource consumption.

Alternative Approach using a Lookup Table

To optimize this query, an alternative approach is recommended: creating a separate lookup table called options that maps option IDs to the maximum IDs within the records table. Introducing a foreign key constraint between records.option_id and options.option_id will ensure referential integrity.

CREATE TABLE options (
  option_id int PRIMARY KEY,
  option text UNIQUE NOT NULL
);

INSERT INTO options (option_id, option)
SELECT DISTINCT option_id, 'option' || option_id
FROM records;

Optimized Query using Correlated Subquery

With the options table in place, the original query can be rewritten using a correlated subquery that efficiently joins the two tables based on the option_id field.

SELECT o.option_id, (SELECT MAX(id) FROM records WHERE option_id = o.option_id) AS max_id
FROM options o
ORDER BY o.option_id;

Benefits of the Alternative Approach

This alternative approach offers several advantages:

• Reduces table scans by only accessing the relevant rows in the options table.
• Utilizes efficient correlated subqueries to retrieve the maximum id values from records.
• Maintains referential integrity through the foreign key constraint.

Additional Optimization

Adding an index to the records table on (option_id, id DESC NULLS LAST) can further enhance performance by allowing Postgres to perform index-only scans for the subquery.

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