How Can I Optimize Groupwise Maximum Queries in MySQL and PostgreSQL?

Optimizing Groupwise Maximum Query

The provided query, aiming to retrieve records with maximum IDs grouped by option_id, faces performance issues due to scanning all rows. To address this, consider utilizing a programmatic index that stores maximum ID values for each option_id. This approach enables efficient retrieval of maximums by only scanning the index.

Traditional approaches, such as indexing (option_id, id) or (option_id, id DESC), do not adequately optimize the query.

MySQL's Optimization

MySQL 5.5 incorporates an advanced optimization for this query. By indexing (option_id, id), MySQL leverages the index for group-by operations, avoiding the need for sequential scans.

Postgres Optimization with Reference Table

In Postgres, create a reference table, options, with unique option_ids that correspond to distinct option_ids in the records table.

CREATE TABLE options ( option_id INT PRIMARY KEY, option TEXT UNIQUE NOT NULL );

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

Use a correlated subquery to retrieve maximum IDs from records for each option_id:

SELECT option_id, (SELECT MAX(id) FROM records WHERE option_id = o.option_id) AS max_id
FROM options o
ORDER BY 1;

This approach enables index-only scans, minimizing row access in records. The ideal index for this query:

CREATE INDEX ON records (option_id, id DESC NULLS LAST);

Postgres Optimization with LATERAL Join (Postgres 9.3 )

Alternatively, in Postgres 9.3 and above, use a LATERAL join to achieve a similar optimization:

SELECT *
FROM records
LATERAL (
  SELECT MAX(id) OVER (PARTITION BY option_id) AS max_id
  FROM records
) AS m
WHERE records.id = m.max_id;

This approach also leverages index-only scans for efficient query execution.

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