How to implement multi-value association query through FIND_IN_SET?
Apr 08, 2025 am 09:33 AM
FIND_IN_SET: Solve the mystery of multi-value association query
Many friends will encounter a difficult problem in database operations: How to efficiently handle multi-value association query? For example, a user can have multiple tags. How to find users based on tags? This article will explore in-depth how to use MySQL's FIND_IN_SET
function to gracefully solve this problem and reveal the pitfalls and optimization strategies behind it.
Let's make it clear first: FIND_IN_SET
is not the best solution to deal with multi-value correlation queries. It has performance bottlenecks, especially when the data volume is huge. But understanding how it works and limitations is crucial for database design and optimization. It is more suitable for some special scenarios, such as small data volume or temporary queries, rather than long-term dependencies.
Review of basic knowledge:
The function of the FIND_IN_SET
function is to determine whether a string is in a comma-separated string list. Its syntax is simple: FIND_IN_SET(str,strlist)
, where str
is the string to be looked for and strlist
is a comma-separated list of strings. If str
is in strlist
, return the position of str
in the list (starting from 1); otherwise return 0.
Core concepts and working principles:
The core of FIND_IN_SET
lies in string matching. It is essentially a string lookup operation, not a native associated query of the database. MySQL compares each element in str
to strlist
one by one until a match is found or the full list is traversed. This determines that its efficiency is proportional to the length of the list, and the longer the list, the lower the efficiency. Worse, FIND_IN_SET
cannot take advantage of database indexing, which makes it very slow to query on large datasets.
Code example:
Suppose we have two tables: users
and user_tags
. The users
table contains the user ID and username, user_tags
table contains the user ID and comma-separated tag list.
<code class="sql">-- users 表<br>CREATE TABLE users (</code><pre class='brush:php;toolbar:false;'> user_id INT PRIMARY KEY, username VARCHAR(255)
);
-- user_tags table
CREATE TABLE user_tags (
user_id INT, tags VARCHAR(255)
);
-- Insert some data
INSERT INTO users (user_id, username) VALUES (1, 'Alice'), (2, 'Bob'), (3, 'Charlie');
INSERT INTO user_tags (user_id, tags) VALUES (1, 'tag1,tag2'), (2, 'tag2,tag3'), (3, 'tag1,tag3');
-- Use FIND_IN_SET to query users with the 'tag1' tag
SELECT * FROM users WHERE user_id IN (SELECT user_id FROM user_tags WHERE FIND_IN_SET('tag1', tags) > 0);
This code first filters out the user ID containing the 'tag1' tag from user_tags
table, and then uses IN
clause to find the corresponding user in the users
table. Although this achieves the goal, it is inefficient.
Advanced usage and potential problems:
Does FIND_IN_SET
support wildcard matching? Not supported! This further limits its application scenarios. If you need fuzzy matching, you have to process the string first and then do the matching, which will reduce efficiency.
Performance optimization and best practices:
Avoid using FIND_IN_SET
for multi-value association queries! This is the most important advice. The correct way is to transform the user_tags
table into a standardized database design: create an intermediate table user_tag_mapping
, which contains two columns: user_id
and tag_id
, where tag_id
is the ID of the tag. This allows database indexing to be used to achieve efficient association query.
<code class="sql">-- user_tag_mapping 表<br>CREATE TABLE user_tag_mapping (</code><pre class='brush:php;toolbar:false;'> user_id INT, tag_id INT, PRIMARY KEY (user_id, tag_id)
);
-- tags table
CREATE TABLE tags (
tag_id INT PRIMARY KEY, tag_name VARCHAR(255)
);
-- Reinsert data (need to create tags table first and insert tag1, tag2, tag3)
INSERT INTO user_tag_mapping (user_id, tag_id) VALUES (1, 1), (1, 2), (2, 2), (2, 3), (3, 1), (3, 3);
-- Efficient association query
SELECT u.* FROM users u JOIN user_tag_mapping utm ON u.user_id = utm.user_id JOIN tags t ON utm.tag_id = t.tag_id WHERE t.tag_name = 'tag1';
This standardized design significantly improves query efficiency and avoids the performance bottlenecks brought by FIND_IN_SET
. Remember, database design is the cornerstone of performance optimization. Choosing the right database structure is far more important than dependent on skill functions. Never sacrifice long-term performance and maintainability for temporary convenience.
The above is the detailed content of How to implement multi-value association query through FIND_IN_SET?. For more information, please follow other related articles on the PHP Chinese website!

Hot AI Tools

Undress AI Tool
Undress images for free

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

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

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)

1. The first choice for the Laravel MySQL Vue/React combination in the PHP development question and answer community is the first choice for Laravel MySQL Vue/React combination, due to its maturity in the ecosystem and high development efficiency; 2. High performance requires dependence on cache (Redis), database optimization, CDN and asynchronous queues; 3. Security must be done with input filtering, CSRF protection, HTTPS, password encryption and permission control; 4. Money optional advertising, member subscription, rewards, commissions, knowledge payment and other models, the core is to match community tone and user needs.

There are three main ways to set environment variables in PHP: 1. Global configuration through php.ini; 2. Passed through a web server (such as SetEnv of Apache or fastcgi_param of Nginx); 3. Use putenv() function in PHP scripts. Among them, php.ini is suitable for global and infrequently changing configurations, web server configuration is suitable for scenarios that need to be isolated, and putenv() is suitable for temporary variables. Persistence policies include configuration files (such as php.ini or web server configuration), .env files are loaded with dotenv library, and dynamic injection of variables in CI/CD processes. Security management sensitive information should be avoided hard-coded, and it is recommended to use.en

To achieve MySQL deployment automation, the key is to use Terraform to define resources, Ansible management configuration, Git for version control, and strengthen security and permission management. 1. Use Terraform to define MySQL instances, such as the version, type, access control and other resource attributes of AWSRDS; 2. Use AnsiblePlaybook to realize detailed configurations such as database user creation, permission settings, etc.; 3. All configuration files are included in Git management, support change tracking and collaborative development; 4. Avoid hard-coded sensitive information, use Vault or AnsibleVault to manage passwords, and set access control and minimum permission principles.

Why do I need SSL/TLS encryption MySQL connection? Because unencrypted connections may cause sensitive data to be intercepted, enabling SSL/TLS can prevent man-in-the-middle attacks and meet compliance requirements; 2. How to configure SSL/TLS for MySQL? You need to generate a certificate and a private key, modify the configuration file to specify the ssl-ca, ssl-cert and ssl-key paths and restart the service; 3. How to force SSL when the client connects? Implemented by specifying REQUIRESSL or REQUIREX509 when creating a user; 4. Details that are easily overlooked in SSL configuration include certificate path permissions, certificate expiration issues, and client configuration requirements.

To collect user behavior data, you need to record browsing, search, purchase and other information into the database through PHP, and clean and analyze it to explore interest preferences; 2. The selection of recommendation algorithms should be determined based on data characteristics: based on content, collaborative filtering, rules or mixed recommendations; 3. Collaborative filtering can be implemented in PHP to calculate user cosine similarity, select K nearest neighbors, weighted prediction scores and recommend high-scoring products; 4. Performance evaluation uses accuracy, recall, F1 value and CTR, conversion rate and verify the effect through A/B tests; 5. Cold start problems can be alleviated through product attributes, user registration information, popular recommendations and expert evaluations; 6. Performance optimization methods include cached recommendation results, asynchronous processing, distributed computing and SQL query optimization, thereby improving recommendation efficiency and user experience.

When choosing a suitable PHP framework, you need to consider comprehensively according to project needs: Laravel is suitable for rapid development and provides EloquentORM and Blade template engines, which are convenient for database operation and dynamic form rendering; Symfony is more flexible and suitable for complex systems; CodeIgniter is lightweight and suitable for simple applications with high performance requirements. 2. To ensure the accuracy of AI models, we need to start with high-quality data training, reasonable selection of evaluation indicators (such as accuracy, recall, F1 value), regular performance evaluation and model tuning, and ensure code quality through unit testing and integration testing, while continuously monitoring the input data to prevent data drift. 3. Many measures are required to protect user privacy: encrypt and store sensitive data (such as AES

PHP plays the role of connector and brain center in intelligent customer service, responsible for connecting front-end input, database storage and external AI services; 2. When implementing it, it is necessary to build a multi-layer architecture: the front-end receives user messages, the PHP back-end preprocesses and routes requests, first matches the local knowledge base, and misses, call external AI services such as OpenAI or Dialogflow to obtain intelligent reply; 3. Session management is written to MySQL and other databases by PHP to ensure context continuity; 4. Integrated AI services need to use Guzzle to send HTTP requests, safely store APIKeys, and do a good job of error handling and response analysis; 5. Database design must include sessions, messages, knowledge bases, and user tables, reasonably build indexes, ensure security and performance, and support robot memory

To enable PHP containers to support automatic construction, the core lies in configuring the continuous integration (CI) process. 1. Use Dockerfile to define the PHP environment, including basic image, extension installation, dependency management and permission settings; 2. Configure CI/CD tools such as GitLabCI, and define the build, test and deployment stages through the .gitlab-ci.yml file to achieve automatic construction, testing and deployment; 3. Integrate test frameworks such as PHPUnit to ensure that tests are automatically run after code changes; 4. Use automated deployment strategies such as Kubernetes to define deployment configuration through the deployment.yaml file; 5. Optimize Dockerfile and adopt multi-stage construction
