#Everything we see and get is very scientific#Hello everyone, I am Xiaomi! Today, I will continue to discuss a topic that captivates tech enthusiasts: how to achieve data hotspot separation between MySQL and Redis, ensuring that data in Redis is always hot data! I believe you have already had some ideas in the previous discussion, but today I will synthesize all the previous content to present a more complete solution for everyone. Don't talk too much nonsense, let's start!Background IntroductionIn modern applications, a large amount of data needs to be efficiently stored and accessed

#Everything we see and get is very scientific#

Hello everyone, I am Xiaomi! Today, I will continue to discuss a topic that captivates tech enthusiasts: how to achieve data hotspot separation between MySQL and Redis, ensuring that data in Redis is always hot data! I believe you have already had some ideas in the previous discussion, but today I will synthesize all the previous content to present a more complete solution for everyone. Don't talk too much nonsense, let's start!

Background Introduction

In modern applications, a large amount of data needs to be efficiently stored and accessed. As the two core components, database and cache play crucial roles. However, for the storage and access of massive data, we have to face a reality: not all data is "hot data", that is, frequently accessed. How to efficiently combine massive data in MySQL with hot data in Redis has become a challenging topic.

Option 1: Data Classification

Firstly, we need to clarifyWhich data is hot and which data is non hot. Through dataVisit frequency, recent visit timeClassify hot data and non hot data using indicators such as. This lays the foundation for subsequent operations.

Option 2: Cache Layering Strategy

When solving data hotspots, cache layering strategy is a crucial tool that cannot be ignored.It divides caching into different levels, from local caching, distributed caching, to remote caching, each playing a different role.This can not only improve the efficiency of caching, but also more flexibly adapt to different business needs.

• First level cache:Introduce local caching and use data structures in the application's memory, such as Maps, to store hotspot data. This can quickly respond to application read requests and reduce access pressure on distributed caching. Ensure that the local cache is synchronized with the data in Redis to maintain data consistency.
• L2 cache:Continue to use high-performance Redis as a distributed cache to store global hotspot data. By using the previously mentioned data synchronization mechanism, keep the data in Redis synchronized with the hot data in MySQL.
• Third level cache:Based on actual needs, consider introducing more remote caching, such as Memcached. Remote caching can further improve the cache hit rate, alleviate the pressure of distributed caching, and thus enhance the overall performance of the system.

Option 3: Data synchronization and elimination strategy

To ensure data consistency, it is necessary to establish a data synchronization mechanism to synchronize hot data in MySQL in real-time to the cache. This can be achieved through database triggers, scheduled tasks, and other methods. At the same time, according to business needs, select appropriate data elimination strategies such as LRU, LFU, TTL, etc. to ensure that valuable data is stored in the cache.

Option 4: Separation and optimization of read and write

For large systems,Consider implementing read write separation in MySQL and assigning read and write operations to different MySQL instances. This helps to reduce the pressure on the database, improve read performance, and reduce frequent updates to the cache, which is beneficial for the stability of the data in the cache.

Implementation steps

1. Database Design:In MySQL, create hot and non hot data tables, and design table structures to maximize data reading efficiency.
2. Introducing Local Caching:Introducing Local CachingRedis
3. Using distributed caching:Continue to use Redis as a distributed cache to achieve real-time synchronization of data and efficient use of cache.
4. Introducing Remote Caching:Introducing Remote CachingMemcached
5. Data synchronization and obsolescence strategies:Establish a data synchronization mechanism and select appropriate data elimination strategies to ensure that the data in the cache remains in a hot state.
6. Read write separation and optimization:Implement MySQL read write separation, optimize database performance, and reduce frequent cache updates.

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By comprehensively applying the above strategies, we can achieve data hotspot separation between MySQL and Redis, ensuring that all data in Redis is hotspot data. This can not only improve the performance and response speed of the system, but also better meet different business needs. I hope today's sharing is helpful to you! If you have any questions or ideas, please feel free to communicate and discuss with me in the message area! See you next time. May everyone continue to explore and make progress in the world of technology! Bye bye!

Knowing what it is and why it is

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