


How to Correctly Sum Aggregate Values from Joined Tables in MySQL to Avoid Cross Product Issues?
Using SUM aggregation to join tables in MySQL: solving cross-product problems
Combining two queries using the SUM aggregate function often presents challenges in MySQL. Cross products between tables can cause incorrect SUM values. To solve this problem, the SUM function needs to be encapsulated into a subquery.
Consider the following example where two queries retrieve SUM(drive_time) and SUM(tm_hours) respectively for a specific date and a teacher ID of 5:
Query 1:
SELECT last_name, first_name, DATE_FORMAT(mil_date, '%m/%d/%y') AS dates, SUM(drive_time) MINUTES FROM bhds_mileage LEFT JOIN bhds_teachers i ON i.ds_id = bhds_mileage.ds_id WHERE mil_date BETWEEN '2016-04-11' AND '2016-04-30' AND bhds_mileage.ds_id = 5 GROUP BY CONCAT(YEAR(mil_date), '/', WEEK(mil_date)), bhds_mileage.ds_id ORDER BY last_name ASC, dates ASC
Query 2:
SELECT last_name, first_name, DATE_FORMAT(tm_date, '%m/%d/%y') AS dates, SUM(tm_hours) total FROM bhds_timecard LEFT JOIN bhds_teachers i ON i.ds_id = bhds_timecard.ds_id WHERE tm_date BETWEEN '2016-04-11' AND '2016-04-30' AND bhds_timecard.ds_id = 5 GROUP BY CONCAT(YEAR(tm_date), '/', WEEK(tm_date)), bhds_timecard.ds_id ORDER BY last_name ASC, dates ASC
Simple connection attempt:
To combine these queries, a simple way is to concatenate them as follows:
SELECT last_name, first_name, DATE_FORMAT(tm_date, '%m/%d/%y') AS dates, SUM(tm_hours) total, SUM(drive_time) MINUTES FROM bhds_timecard LEFT JOIN bhds_teachers i ON i.ds_id = bhds_timecard.ds_id LEFT JOIN bhds_mileage ON DATE_FORMAT(bhds_timecard.tm_date, '%m/%d/%y') = DATE_FORMAT(bhds_mileage.mil_date, '%m/%d/%y') AND bhds_timecard.ds_id = bhds_mileage.ds_id WHERE tm_date BETWEEN '2016-04-11' AND '2016-04-30' AND bhds_timecard.ds_id = 5 GROUP BY CONCAT(YEAR(tm_date), '/', WEEK(tm_date)), bhds_timecard.ds_id
However, this approach creates cross products between tables, resulting in incorrect SUM values.
Solution:
To get correct SUM values, move the SUM aggregate function into a subquery. This prevents cross-product problems and ensures that SUM values are only calculated in relevant rows:
SELECT last_name, first_name, DATE_FORMAT(LEAST(mil_date, tm_date), '%m/%d/%y') AS dates, total, minutes FROM bhds_teachers AS i LEFT JOIN ( SELECT ds_id, YEARWEEK(mil_date) AS week, MIN(mil_date) AS mil_date, SUM(drive_time) AS minutes FROM bhds_mileage WHERE mil_date BETWEEN '2016-04-11' AND '2016-04-30' AND bhds_mileage.ds_id = 5 GROUP BY ds_id, week ) AS m ON m.ds_id = i.ds_id LEFT JOIN ( SELECT ds_id, YEARWEEK(tm_date) AS week, MIN(tm_date) AS tm_date, SUM(tm_hours) AS total FROM bhds_timecard WHERE tm_date BETWEEN '2016-04-11' AND '2016-04-30' AND bhds_timecard.ds_id = 5 GROUP BY ds_id, week ) AS t ON t.ds_id = i.ds_id AND t.week = m.week
The above is the detailed content of How to Correctly Sum Aggregate Values from Joined Tables in MySQL to Avoid Cross Product Issues?. 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

Video Face Swap
Swap faces in any video effortlessly with our completely free AI face swap tool!

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 main role of MySQL in web applications is to store and manage data. 1.MySQL efficiently processes user information, product catalogs, transaction records and other data. 2. Through SQL query, developers can extract information from the database to generate dynamic content. 3.MySQL works based on the client-server model to ensure acceptable query speed.

InnoDB uses redologs and undologs to ensure data consistency and reliability. 1.redologs record data page modification to ensure crash recovery and transaction persistence. 2.undologs records the original data value and supports transaction rollback and MVCC.

Compared with other programming languages, MySQL is mainly used to store and manage data, while other languages such as Python, Java, and C are used for logical processing and application development. MySQL is known for its high performance, scalability and cross-platform support, suitable for data management needs, while other languages have advantages in their respective fields such as data analytics, enterprise applications, and system programming.

MySQL index cardinality has a significant impact on query performance: 1. High cardinality index can more effectively narrow the data range and improve query efficiency; 2. Low cardinality index may lead to full table scanning and reduce query performance; 3. In joint index, high cardinality sequences should be placed in front to optimize query.

The basic operations of MySQL include creating databases, tables, and using SQL to perform CRUD operations on data. 1. Create a database: CREATEDATABASEmy_first_db; 2. Create a table: CREATETABLEbooks(idINTAUTO_INCREMENTPRIMARYKEY, titleVARCHAR(100)NOTNULL, authorVARCHAR(100)NOTNULL, published_yearINT); 3. Insert data: INSERTINTObooks(title, author, published_year)VA

MySQL is suitable for web applications and content management systems and is popular for its open source, high performance and ease of use. 1) Compared with PostgreSQL, MySQL performs better in simple queries and high concurrent read operations. 2) Compared with Oracle, MySQL is more popular among small and medium-sized enterprises because of its open source and low cost. 3) Compared with Microsoft SQL Server, MySQL is more suitable for cross-platform applications. 4) Unlike MongoDB, MySQL is more suitable for structured data and transaction processing.

InnoDBBufferPool reduces disk I/O by caching data and indexing pages, improving database performance. Its working principle includes: 1. Data reading: Read data from BufferPool; 2. Data writing: After modifying the data, write to BufferPool and refresh it to disk regularly; 3. Cache management: Use the LRU algorithm to manage cache pages; 4. Reading mechanism: Load adjacent data pages in advance. By sizing the BufferPool and using multiple instances, database performance can be optimized.

MySQL efficiently manages structured data through table structure and SQL query, and implements inter-table relationships through foreign keys. 1. Define the data format and type when creating a table. 2. Use foreign keys to establish relationships between tables. 3. Improve performance through indexing and query optimization. 4. Regularly backup and monitor databases to ensure data security and performance optimization.
