How to use object caching for persistent storage
Jul 03, 2025 am 12:23 AMObject cache assists persistent storage, suitable for high access and low updates, tolerating short-term lost data. 1. Data suitable for "persistence" in cache includes user configuration, popular product information, etc., which can be restored from the database but can be accelerated by using cache. 2. Select a cache backend that supports persistence, enable RDB or AOF mode, and configure a reasonable expiration policy, but it cannot replace the main database. 3. Set long TTL or never expired keys, adopt clear key name structure such as user:1001:profile, and update the cache synchronously when modifying data. 4. It can combine local and distributed caches to store small data locally and big data Redis to store big data and use it for recovery after restart, while paying attention to consistency and resource usage issues.
Object caching is often used to improve performance, but many people ignore it as an auxiliary tool for persistent storage. Although object cache is not designed for long-term storage, in some scenarios, rational use of it can reduce database pressure, speed up access, and even retain some data state during a brief power outage or service restart.
1. Clarify the scope of application of object cache
Not all data is suitable for "persistence" with object cache. It's better for those:
- Data with high access frequency but infrequent updates
- Tolerate short-term loss (such as regenerating) data
- As a temporary acceleration layer outside the database exists
For example: configuration information after user login, basic information of popular products, etc. This data can be loaded again from the database even if the cache fails, but using cache can significantly improve response speed.
2. Select the appropriate cache backend to support persistence
Most memory caches (such as Redis, Memcached) are volatile by default, but Redis can achieve a certain degree of persistence through configuration. If you want object cache to have certain persistence:
- Enable Redis's RDB snapshot feature to write cached content to disk regularly
- Or turn on AOF log mode and record each write operation to restore data
- Configure appropriate expiration policies to avoid caches automatically clean up critical data
Note: Even if persistence is enabled, cache should not be fully reliant on as primary storage. It is still an auxiliary mechanism, not a reason to replace the database.
3. Set reasonable cache life cycle and key naming rules
To better manage persistent data in cache, it is recommended:
- Set a long TTL (survival time) for caches that need to be retained for a long time, or set it to never expire (but be careful about memory usage)
- Use clear key name structure, such as
user:1001:profile
, which facilitates maintenance and troubleshooting of problems - For data that needs to be updated synchronously, actively update or clear the corresponding cache while modifying the database.
For example: When a user changes his nickname, in addition to updating the database, the user information in the cache should be refreshed immediately, otherwise the user may see the old data, causing inconsistency.
4. Combining local cache and distributed cache usage
Some applications use both local caches (such as APCu, memory dictionary) and distributed caches (such as Redis). In this case, you can:
- Put the small data you access most frequently in the local cache to reduce network requests
- Put larger data that needs to be shared by multiple nodes in Redis
- Try to restore local cached content from Redis when service restarts
This method can take into account both speed and availability, but also pay attention to consistency control and resource utilization.
Basically that's it. Although object cache is not the only way to do persistent storage, it can play a good role as long as you use the right scenario and match the strategies.
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