


How to Dynamically Convert Rows to Columns in MySQL Based on Multiple Columns?
Dynamic Conversion of Rows to Columns Based on Multiple Columns in MySQL
In a previous question, a MySQL query was used to dynamically convert rows to columns for a single column. Now, we aim to achieve the same result for two columns: "data" and "price."
Problem:
Convert the data in the following table into columns named "order1," "order2," "order3," "item1," "item2," "item3," and "item4" based on the "order" and "item" columns.
id | order | data | item | Price |
---|---|---|---|---|
1 | 1 | P | 1 | 50 |
1 | 1 | P | 2 | 60 |
1 | 1 | P | 3 | 70 |
1 | 2 | Q | 1 | 50 |
1 | 2 | Q | 2 | 60 |
1 | 2 | Q | 3 | 70 |
2 | 1 | P | 1 | 50 |
2 | 1 | P | 2 | 60 |
2 | 1 | P | 4 | 80 |
2 | 3 | S | 1 | 50 |
2 | 3 | S | 2 | 60 |
2 | 3 | S | 4 | 80 |
Desired Result:
id | order1 | order2 | order3 | item1 | item2 | item3 | item4 |
---|---|---|---|---|---|---|---|
1 | P | Q | 50 | 60 | 70 | ||
2 | P | S | 50 | 60 | 80 |
Solution:
While a hard-coded query could be used if the number of "order" and "item" values is known, a more dynamic solution is required in the general case.
Unpivoting the Data:
MySQL lacks an unpivot function, but a UNION ALL can be used to convert the multiple data pairs into rows:
select id, concat('order', `order`) col, data value from tableA union all select id, concat('item', item) col, price value from tableA;
Converting Values Back into Columns:
The unpivoted data can then be converted back into columns using an aggregate function with CASE:
select id, max(case when col = 'order1' then value end) order1, max(case when col = 'order2' then value end) order2, max(case when col = 'order3' then value end) order3, max(case when col = 'item1' then value end) item1, max(case when col = 'item2' then value end) item2, max(case when col = 'item3' then value end) item3 from ( select id, concat('order', `order`) col, data value from tableA union all select id, concat('item', item) col, price value from tableA ) d group by id;
Dynamic Prepared Statement:
Finally, the query can be converted into a dynamic prepared statement using set-based concatenation and EXECUTE:
SET @sql = NULL; SELECT GROUP_CONCAT(DISTINCT CONCAT( 'max(case when col = ''', col, ''' then value end) as `', col, '`') ) INTO @sql FROM ( select concat('order', `order`) col from tableA union all select concat('item', `item`) col from tableA )d; SET @sql = CONCAT('SELECT id, ', @sql, ' from ( select id, concat(''order'', `order`) col, data value from tableA union all select id, concat(''item'', item) col, price value from tableA ) d group by id'); PREPARE stmt FROM @sql; EXECUTE stmt; DEALLOCATE PREPARE stmt;
This approach provides a flexible and dynamic way to convert rows to columns based on multiple criteria in MySQL.
The above is the detailed content of How to Dynamically Convert Rows to Columns in MySQL Based on Multiple Columns?. 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.
