The difference between redis cache breakdown and cache penetration
The difference between cache breakdown and cache penetration: Cache breakdown: occurs when a large number of concurrent requests access uncached keys, causing excessive pressure on the database. Cache penetration: occurs when malicious requests or crawlers frequently access keys that have never been cached, resulting in unnecessary database access.
The difference between Redis cache penetration and cache penetration
Question: Cache penetration What is the difference between penetration and cache penetration?
Answer:
Cache penetration and Cache penetration are two different cache problems:
Cache breakdown
- Description: When a specific key does not exist in the cache, and there are a large number of concurrent requests accessing it at the same time in a short period of time When this key is entered, a cache breakdown occurs.
- Cause: Because the key is not in the cache, all requests directly access the database, resulting in excessive pressure on the database and even a crash.
- Solution: Use a locking mechanism or other means to prevent concurrent requests from accessing uncached keys at the same time, and handle cache misses.
Cache Penetration
- Description: When certain keys have never been cached and are accessed through malicious requests or Cache penetration occurs when crawlers access frequently.
- Cause: The key does not exist in the cache, all requests will directly access the database, even if the key does not exist.
- Solution: Check the validity of all requests, and directly return errors or null values for non-existent keys to avoid unnecessary database access.
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Redis's database methods include in-memory databases and key-value storage. 1) Redis stores data in memory, and reads and writes fast. 2) It uses key-value pairs to store data, supports complex data structures such as lists, collections, hash tables and ordered collections, suitable for caches and NoSQL databases.

Redis is a powerful database solution because it provides fast performance, rich data structures, high availability and scalability, persistence capabilities, and a wide range of ecosystem support. 1) Extremely fast performance: Redis's data is stored in memory and has extremely fast read and write speeds, suitable for high concurrency and low latency applications. 2) Rich data structure: supports multiple data types, such as lists, collections, etc., which are suitable for a variety of scenarios. 3) High availability and scalability: supports master-slave replication and cluster mode to achieve high availability and horizontal scalability. 4) Persistence and data security: Data persistence is achieved through RDB and AOF to ensure data integrity and reliability. 5) Wide ecosystem and community support: with a huge ecosystem and active community,

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The core function of Redis is a high-performance in-memory data storage and processing system. 1) High-speed data access: Redis stores data in memory and provides microsecond-level read and write speed. 2) Rich data structure: supports strings, lists, collections, etc., and adapts to a variety of application scenarios. 3) Persistence: Persist data to disk through RDB and AOF. 4) Publish subscription: Can be used in message queues or real-time communication systems.

Redis supports a variety of data structures, including: 1. String, suitable for storing single-value data; 2. List, suitable for queues and stacks; 3. Set, used for storing non-duplicate data; 4. Ordered Set, suitable for ranking lists and priority queues; 5. Hash table, suitable for storing object or structured data.

Redis counter is a mechanism that uses Redis key-value pair storage to implement counting operations, including the following steps: creating counter keys, increasing counts, decreasing counts, resetting counts, and obtaining counts. The advantages of Redis counters include fast speed, high concurrency, durability and simplicity and ease of use. It can be used in scenarios such as user access counting, real-time metric tracking, game scores and rankings, and order processing counting.


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