


How Can I Optimize GROUP BY Queries to Efficiently Retrieve the Latest Row for Each User?
Optimization Strategies for GROUP BY Query to Retrieve Latest Row per User
Given a table with user messages structured as log_date, user_id, and payload, the task is to efficiently retrieve the latest record for each user before a specific date.
Multicolumn Index
To improve read performance, create a multicolumn index on user_id and log_date:
CREATE INDEX log_combo_idx ON log (user_id, log_date DESC NULLS LAST);
Index-Only Scans with Covering Index
For index-only scans, define a covering index that includes the payload column:
CREATE INDEX log_combo_covering_idx ON log (user_id, log_date DESC NULLS LAST) INCLUDE (payload);
SELECT DISTINCT ON()
For small tables or few rows per user_id, using SELECT DISTINCT ON() can be efficient:
SELECT DISTINCT ON(user_id) log_date, payload FROM log WHERE log_date <= :mydate ORDER BY user_id, log_date DESC;
Index Skip Scan Emulation
For large tables with many rows per user_id, consider emulating an index skip scan using a recursive CTE with LATERAL join:
WITH RECURSIVE cte AS ( ( SELECT user_id, log_date, payload FROM log WHERE log_date <= :mydate ORDER BY user_id, log_date DESC NULLS LAST LIMIT 1 ) UNION ALL SELECT l.* FROM cte c CROSS JOIN LATERAL ( SELECT l.user_id, l.log_date, l.payload FROM log l WHERE l.user_id > c.user_id -- lateral reference AND log_date <= :mydate -- repeat condition ORDER BY l.user_id, l.log_date DESC NULLS LAST LIMIT 1 ) l ) TABLE cte ORDER BY user_id;
Separate Users Table
If a separate users table exists, simplified solutions are possible:
LATERAL Join
SELECT u.user_id, l.log_date, l.payload FROM users u CROSS JOIN LATERAL ( SELECT l.log_date, l.payload FROM log l WHERE l.user_id = u.user_id -- lateral reference AND l.log_date <= :mydate ORDER BY l.log_date DESC NULLS LAST LIMIT 1 ) l;
Correlated Subquery
SELECT user_id, (combo1).* -- note parentheses FROM ( SELECT u.user_id , (SELECT (l.log_date, l.payload)::combo FROM log l WHERE l.user_id = u.user_id AND l.log_date <= :mydate ORDER BY l.log_date DESC NULLS LAST LIMIT 1) AS combo1 FROM users u ) sub;
These optimizations improve query performance by utilizing indexes, emulating skip scans, and taking advantage of a separate table for user information.
The above is the detailed content of How Can I Optimize GROUP BY Queries to Efficiently Retrieve the Latest Row for Each User?. For more information, please follow other related articles on the PHP Chinese website!

Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

AI Hentai Generator
Generate AI Hentai for free.

Hot Article

Hot Tools

Notepad++7.3.1
Easy-to-use and free code editor

SublimeText3 Chinese version
Chinese version, very easy to use

Zend Studio 13.0.1
Powerful PHP integrated development environment

Dreamweaver CS6
Visual web development tools

SublimeText3 Mac version
God-level code editing software (SublimeText3)

Hot Topics



The article discusses using MySQL's ALTER TABLE statement to modify tables, including adding/dropping columns, renaming tables/columns, and changing column data types.

Article discusses configuring SSL/TLS encryption for MySQL, including certificate generation and verification. Main issue is using self-signed certificates' security implications.[Character count: 159]

Article discusses popular MySQL GUI tools like MySQL Workbench and phpMyAdmin, comparing their features and suitability for beginners and advanced users.[159 characters]

Article discusses strategies for handling large datasets in MySQL, including partitioning, sharding, indexing, and query optimization.

InnoDB's full-text search capabilities are very powerful, which can significantly improve database query efficiency and ability to process large amounts of text data. 1) InnoDB implements full-text search through inverted indexing, supporting basic and advanced search queries. 2) Use MATCH and AGAINST keywords to search, support Boolean mode and phrase search. 3) Optimization methods include using word segmentation technology, periodic rebuilding of indexes and adjusting cache size to improve performance and accuracy.

The article discusses dropping tables in MySQL using the DROP TABLE statement, emphasizing precautions and risks. It highlights that the action is irreversible without backups, detailing recovery methods and potential production environment hazards.

Article discusses using foreign keys to represent relationships in databases, focusing on best practices, data integrity, and common pitfalls to avoid.

The article discusses creating indexes on JSON columns in various databases like PostgreSQL, MySQL, and MongoDB to enhance query performance. It explains the syntax and benefits of indexing specific JSON paths, and lists supported database systems.
