Data synchronization between Redis and traditional databases involves how to maintain data consistency between cached and persistent storage. This is especially important in high concurrency environments. Here are several common methods of Redis and database synchronization:
1. Cache Aside (Lazy Loading)
This is the most commonly used strategy, also known asRead and write penetrationmodel.
Read operation：
- The application reads data from Redis first.
- If there is no data in Redis (cache missed), it is read from the database.
- Writes data to the Redis cache after reading.
Write operation：
- Update the database.
- Delete or update cached data in Redis after success.
advantage: The cache is only loaded when needed, reducing unnecessary cache usage.
shortcoming: Can cause temporary inconsistencies, as database updates and cache updates are two steps.
2. Write Through
This pattern ensures that data is written to both the cache and the database at each write operation.
Read operation: Read directly from Redis.
Write operation：
- The data is first written to the Redis cache.
- Redis Synchronizes data to the database.
advantage: Data is kept synchronized between cache and database to reduce inconsistency.
shortcoming: The write operation has a high latency because Redis and database are required to operate simultaneously each time.
3. Write Behind (Write Back)
In this mode, after the application writes data to the Redis cache, the cache synchronizes the data to the database asynchronously.
Read operation: Read data from Redis.
Write operation：
- Data is written to Redis.
- Redis asynchronously updates data to the database.
advantage: The write operation is fast, because the writing to the database is performed asynchronously.
shortcoming: May cause data loss (for example, data not synchronized to the database before cache crash).
4. Data consistency challenge
Maintaining consistency between Redis and database data is a challenge, especially in highly concurrency or distributed systems. Common solutions include:
- Distributed transactions: Use a two-stage commit (2PC) or a distributed transaction coordinator to ensure consistency between Redis and database updates.
- Optimistic lock: Through mechanisms such as version number or timestamp, prevent data inconsistency caused by multiple processes when modifying data at the same time.
- Data expiration strategy: Set TTL (Survival Time) for cached data and refresh the cache periodically to ensure consistency with the database.
5. Use message queues
Message queues (such as Kafka, RabbitMQ) ensure reliable synchronization of data between Redis and the database.
Write operation：
- Application writes to Redis.
- At the same time, the write operation is pushed to the message queue.
- Message queue consumers process messages asynchronously and update databases.
advantage: Can effectively handle large amounts of write operations to ensure high availability.
shortcoming: Additional infrastructure and complex management are required.
These strategies need to be trade-offs based on the specific application scenario. Generally, scenarios with more reads and less writes are suitable for using Cache Aside mode, while high concurrent writes may be more suitable for Write Through or using message queues.
Adventure story of data synchronization
In a digital kingdom, the data city is the most prosperous place. There are thousands of database residents living here, the core force of the kingdom. They record every event and every transaction to ensure that the kingdom runs in an orderly manner.
RedisHe is the guard of the data city, and he is responsible for caching data and making data access faster. However, Redis faces a big challenge: how to keep pace with database residents and ensure consistency of information?
Cache Aside (Lazy Loading): Cunning spy
Redis is a smart guard who knows how to save energy. He has a spy calledCache Aside, responsible for passing information between the data city and the cache.
Whenever a visitor comes to request data, Redis sends Cache Aside to his cache repository first. If there is data in the warehouse, the spy will quickly get it back. If not, Cache Aside had to run to the data city, get the latest information from the database residents, and store this information in its own cache repository for next use.
But sometimes, when the database resident updates the information, Cache Aside has not had time to update his cache repository. This can lead to brief information inconsistencies. Still, Cache Aside always corrects it quickly to make sure that the information is accurate most of the time.
Write Through: The Loyal Messenger
Redis also has a loyal messenger namedWrite Through. Whenever a visitor needs to update information, Write Through first sends the information to Redis's cache repository, and then runs non-stop to the data city to pass the same information to the database residents.
In this way, Redis and the database are always in sync, and information inconsistencies are almost never happening. However, Write Through’s responsibilities are heavy because he has to do two things at a time, which leads to him being a little slower than Cache Aside.
Write Behind (Write Back): Fast Courier
Redis also hired a very fast courier namedWrite Behind. He likes to quickly store visitors' information in the cache warehouse first, and then use the late night to quietly send this information to the database residents of the data city.
Because he is very fast, visitors like him very much. But sometimes, if he hasn't had time to send the information out and the cache repository suddenly crashes, the unsent information will be lost forever.
Consistency Challenge: The Distributed Test
As the kingdom develops, the data city becomes larger and larger, Redis faces greater challenges. Whenever high concurrency occurs, information flow becomes extremely complex, and Redis and database residents must work together to ensure that information does not go wrong.
They introducedDistributed transactions, to ensure that each information update is synchronized accurately between the cache and the database. andOptimistic lockIt is like a strict supervisor, ensuring that no data will be modified by multiple people at the same time.
Data expiration strategyHe is a genius responsible for time management. He regularly cleans outdated information in the cache to ensure that the cached data is always synchronized with the database.
Message queue: Organized dispatcher
To ensure more efficient and reliable synchronization, Redis and Data City also hiredMessage QueueAs a dispatcher. Whenever new information needs to be passed, the message queue will arrange the information in the queue and deliver it to the database residents in order.
This allows information to be synchronized to the data city in an orderly manner even in high concurrency, ensuring that the Kingdom's information management is as efficient as ever.
Summarize
Through these different roles, Redis and database residents work together to maintain the prosperity and stability of the data city. With their efforts, the problem of data synchronization was solved one by one, and the operation of the kingdom became smoother. Each synchronization method has its advantages and disadvantages, but as long as it is properly matched, the data synchronization between Redis and the database can be as full of wisdom and miracles as the adventure in the story.
The above is personal experience. I hope you can give you a reference and I hope you can support me more.