1. Scenario:
Double-write consistency means that when we update the data in the database, the data in redis must also be updated synchronously. The process of reading data using redis. When the user accesses the data, the data will be read from the cache first. If the cache is hit, the data in the cache will be directly returned to the user. If there is no data in the cache, the database will be queried first. Save the queried data to the cache and then return it to the user.
2. Strategy to ensure double-write consistency
1. Update the cache first, then update the database
2. Update the database first , then update the cache
3. Delete the cache first, then update the database
4. Update the database first, then delete the cache
3. The advantages and disadvantages of the four strategies
1. Update the cache first, then update the database
The problem is obvious. If the cache is updated successfully but the database update fails, dirty data in the cache will be caused.
2. Update the database first, then update the cache
If the concurrency is high, the following situation may exist. Thread A updates the database. If due to network or other reasons, Thread A has not had time to update the cache. At this time, a process B updates the database and updates the cache. Only then does process A update the cache. This will cause thread B to lose its update to the cache, like a transaction loss situation
3. Delete the cache first, and then update the database
This strategy may have avoided the cache loss in Strategy 2, but under high concurrency conditions , there will also be inconsistencies. For example, when thread A performs a write operation, it first deletes the cache and then prepares to communicate with the new database. At this time, thread B performs a write operation without hitting the cache, and then queries the database. At this time, the old value is read. , and save the queried old value to the cache. Then thread A completes the update of the database. At this time, the database and cache are inconsistent again. Solution: We only need to re-thread A. After completing the update of the database, Delaying and then deleting the cache is also called delayed double deletion. The delay time here must be greater than the time of a read operation of the business.
4. Update the database first, then delete the cache
No matter how high the concurrency is, there will be inconsistencies, such as when thread A reads data. , when preparing to write to the cache, thread B updated the database, and then performed the delete cache operation. At this time, thread A wrote the old value into the cache, although the probability of this happening is relatively low, because the write operation time is greater than the time of a read operation. Solution: Delayed double deletion. Delayed double deletion is still a problem. What should I do if deleting the cache fails? Of course, delete it again and continue deleting in a loop. After the deletion fails, we can put the key to be deleted into the queue, and then try to delete it repeatedly until the deletion is successful.
The above is the detailed content of Ensure the double-write consistency of mysql and redis. For more information, please follow other related articles on the PHP Chinese website!

Redis's data model and structure include five main types: 1. String: used to store text or binary data, and supports atomic operations. 2. List: Ordered elements collection, suitable for queues and stacks. 3. Set: Unordered unique elements set, supporting set operation. 4. Ordered Set (SortedSet): A unique set of elements with scores, suitable for rankings. 5. Hash table (Hash): a collection of key-value pairs, suitable for storing objects.

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,

Key features of Redis include speed, flexibility and rich data structure support. 1) Speed: Redis is an in-memory database, and read and write operations are almost instantaneous, suitable for cache and session management. 2) Flexibility: Supports multiple data structures, such as strings, lists, collections, etc., which are suitable for complex data processing. 3) Data structure support: provides strings, lists, collections, hash tables, etc., which are suitable for different business needs.

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.

Use the Redis command line tool (redis-cli) to manage and operate Redis through the following steps: Connect to the server, specify the address and port. Send commands to the server using the command name and parameters. Use the HELP command to view help information for a specific command. Use the QUIT command to exit the command line tool.


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