In Spring Boot Redis, how to solve the problem of returning garbled codes?
Apr 19, 2025 pm 07:36 PMThe root cause and solution for the keys() method in Spring Boot Redis to return garbled code
When integrating Redis in Spring Boot applications, garbled problems often occur when using redisTemplate.keys()
method to obtain key values. This is mainly because the default key serialization method of RedisTemplate does not match the actual key type. This article will analyze this problem in detail and provide effective solutions.
The root cause of the problem is that developers usually use custom serializers (such as FastJson2JsonRedisSerializer
) to serialize Redis values, but ignore the serialization of keys. redisTemplate.keys()
method directly uses Redis's own serialization mechanism, which will cause garbled code when the key is not a simple string type.
Solution: Set up the key serializer for RedisTemplate correctly
The key to solving this problem is to correctly set the key serializer for RedisTemplate
to ensure that all keys are serialized into strings. Here is a modified example of Redis configuration class, using StringRedisSerializer
to serialize keys and using FastJson2JsonRedisSerializer
to serialize values:
@Configuration public class RedisConfig { @Bean public RedisTemplate<string object> redisTemplate(RedisConnectionFactory redisConnectionFactory) { RedisTemplate<string object> redisTemplate = new RedisTemplate(); redisTemplate.setConnectionFactory(redisConnectionFactory); FastJson2JsonRedisSerializer<object> fastJsonRedisSerializer = new FastJson2JsonRedisSerializer(Object.class); redisTemplate.setValueSerializer(fastJsonRedisSerializer); redisTemplate.setKeySerializer(new StringRedisSerializer()); return redisTemplate; } }</object></string></string>
In this configuration, we use StringRedisSerializer
as keySerializer
to ensure that all keys are serialized into strings, thus avoiding the problem of redisTemplate.keys()
method returning garbled code. setValueSerializer
is used to set the value serializer, FastJson2JsonRedisSerializer
is still used here. The code removes some redundant settings from the original configuration, making the configuration simpler and easier to understand.
Through the above configuration, the redisTemplate.keys()
method will return the correct string type key, thereby effectively solving the garbled problem and ensuring that the Redis key values ??are stored and read in the correct format.
The above is the detailed content of In Spring Boot Redis, how to solve the problem of returning garbled codes?. 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)

Hot Topics

To realize text error correction and syntax optimization with AI, you need to follow the following steps: 1. Select a suitable AI model or API, such as Baidu, Tencent API or open source NLP library; 2. Call the API through PHP's curl or Guzzle and process the return results; 3. Display error correction information in the application and allow users to choose whether to adopt it; 4. Use php-l and PHP_CodeSniffer for syntax detection and code optimization; 5. Continuously collect feedback and update the model or rules to improve the effect. When choosing AIAPI, focus on evaluating accuracy, response speed, price and support for PHP. Code optimization should follow PSR specifications, use cache reasonably, avoid circular queries, review code regularly, and use X

User voice input is captured and sent to the PHP backend through the MediaRecorder API of the front-end JavaScript; 2. PHP saves the audio as a temporary file and calls STTAPI (such as Google or Baidu voice recognition) to convert it into text; 3. PHP sends the text to an AI service (such as OpenAIGPT) to obtain intelligent reply; 4. PHP then calls TTSAPI (such as Baidu or Google voice synthesis) to convert the reply to a voice file; 5. PHP streams the voice file back to the front-end to play, completing interaction. The entire process is dominated by PHP to ensure seamless connection between all links.

PHP does not directly perform AI image processing, but integrates through APIs, because it is good at web development rather than computing-intensive tasks. API integration can achieve professional division of labor, reduce costs, and improve efficiency; 2. Integrating key technologies include using Guzzle or cURL to send HTTP requests, JSON data encoding and decoding, API key security authentication, asynchronous queue processing time-consuming tasks, robust error handling and retry mechanism, image storage and display; 3. Common challenges include API cost out of control, uncontrollable generation results, poor user experience, security risks and difficult data management. The response strategies are setting user quotas and caches, providing propt guidance and multi-picture selection, asynchronous notifications and progress prompts, key environment variable storage and content audit, and cloud storage.

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.

PHP ensures inventory deduction atomicity through database transactions and FORUPDATE row locks to prevent high concurrent overselling; 2. Multi-platform inventory consistency depends on centralized management and event-driven synchronization, combining API/Webhook notifications and message queues to ensure reliable data transmission; 3. The alarm mechanism should set low inventory, zero/negative inventory, unsalable sales, replenishment cycles and abnormal fluctuations strategies in different scenarios, and select DingTalk, SMS or Email Responsible Persons according to the urgency, and the alarm information must be complete and clear to achieve business adaptation and rapid response.

Dogecoin, Pepe and Brett are leading the meme coin craze. Dogecoin (DOGE) is the originator, firmly ranked first in the market value list, Pepe (PEPE) has achieved hundreds of times increase with its social geek culture, and Brett (BRETT) has become popular with its unique visual style as a new star in Base chain; the three were issued in 2013, 2023 and 2024 respectively. Technically, Dogecoin is based on Litecoin, Pepe and Brett are ERC-20 tokens, and the latter relies on the Base chain to improve efficiency. In terms of community, DOGE Twitter fans have exceeded 3 million, Pepe Reddit is leading in activity, Brett's popularity in Base chain, and DOGE has logged in on the platform.

The "reverse reference" in the currency circle, as the name suggests, refers to those reference objects whose views or operations are often opposite to the actual market trend. When such people or groups are extremely optimistic, the market may face a decline; when they are extremely pessimistic, the market may instead rebound. This is not to say that these people deliberately provide wrong signals, but that their judgments may deviate from the mainstream trends in the market, or that their operating behavior happens to be a catalyst for market reversal in a specific situation.

Select the appropriate AI voice recognition service and integrate PHPSDK; 2. Use PHP to call ffmpeg to convert recordings into API-required formats (such as wav); 3. Upload files to cloud storage and call API asynchronous recognition; 4. Analyze JSON results and organize text using NLP technology; 5. Generate Word or Markdown documents to complete the automation of meeting records. The entire process needs to ensure data encryption, access control and compliance to ensure privacy and security.
