How to write the double-write consistency problem in redis database

Redis database double-write consistency can be guaranteed through the following solutions: 1. Optimistic locking: the client obtains the version number, and if it is consistent with the database, writing is allowed; 2. Pessimistic locking: the client obtains exclusive access to data Lock, keep the lock until the write operation is completed; 3. Distributed transaction manager: coordinate write operations across multiple Redis servers, ensuring that all operations either succeed or fail; 4. Data flow replication: direct write operations to the main server , the master server copies data to the slave server to maintain consistency; 5. Persistence: Persist data to disk regularly to recover data in the event of failure or data loss.

Redis database double-write consistency problem

Question:

How to ensure data consistency when using Redis database for double writing?

Solution:

Redis database double-write consistency can be guaranteed through the following solutions:

1. Optimistic Locking

• Each write operation will include a version number used to track the latest state of the data.
• Before writing data, the client will obtain the current version number.
• If the client's version number is the same as the version number stored in the database, the write operation is allowed.
• Otherwise, the write operation will be rejected and the client needs to re-obtain the data and retry the write.

2. Pessimistic Locking

• The client will obtain an exclusive lock on the data before performing any write operations.
• The client holds the lock until the write operation is completed.
• While holding the lock, other clients cannot modify the data to ensure data consistency.

3. Distributed transaction manager

• Use a distributed transaction manager (such as Apache Helix) to coordinate writes across multiple Redis servers operate.
• The transaction manager is responsible for ensuring that all write operations either succeed or fail.
• This ensures that the data remains consistent across all servers.

4. Data flow replication

• Establish a separate Redis server as the main server.
• Direct write operations to the primary server.
• The master server copies data to the slave server.
• Ensure that all write operations arrive at the master server first and then are passed to the slave server through replication to maintain consistency.

5. Persistence

• Regularly persist the data in the Redis database to disk.
• Persistence can help restore a consistent data state even in the event of failure or data loss.

Note:

• Selecting the appropriate solution depends on the specific application and data consistency requirements.
• Optimistic locking is suitable for systems with fewer conflicts, while pessimistic locking is more suitable for systems with more conflicts.
• Distributed transaction managers provide the highest level of data consistency, but also have higher overhead.

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