How to quickly release memory when Redis memory is full?

When Redis is insufficient, it is necessary to delete data first to make room. It can be selectively cleaned according to the data life cycle (expired data is preferred) or hot (less data is preferred). It can also consider using LRU algorithms, optimizing data structures, and monitoring memory usage. In addition, before performing any cleaning operation, be sure to back up the data and test it thoroughly to ensure the data is safe.

Redis memory full? This is a headache, especially in production environments. It is not a joke to directly cause the service to be unavailable. In this article, we will explore in-depth how to solve this problem quickly and effectively and prevent it from happening again. After reading it, you can not only master the emergency response plan, but also understand the root cause of the problem, thereby building a more robust Redis application.

Let’s start with the basics. Redis’s memory model is based on key-value pairs. If the memory is full, it is nothing more than that there are too many key-value pairs stored, or a single key-value pair is too large. Understanding this is very important, it determines our solution. Simply put, if you don’t have enough memory, you have to make room.

The most direct way is of course to delete data. But how to delete it? This requires strategy. Don’t delete it all, it’s no different from restarting the service directly. The risk of data loss is huge. We have to clean up selectively.

One strategy is to clean up based on the life cycle of the data. If your Redis is used as a cache, expired data is the preferred target. You can use the KEYS * command and the EXPIRE command to find and delete expired data. Of course, this is very inefficient. Don't use this directly in the production environment, as Redis can easily get stuck. A better way is to use Redis’s own expiration mechanism to let Redis clean up expired data by itself. This requires you to set the expiration time of the data reasonably to avoid setting too long expiration time to cause excessive memory usage.

Another strategy is to clean up based on the popularity of the data. Which data is used less, please be deleted first. This requires you to have an in-depth understanding of the data access pattern. You can use Redis's MEMORY STATS command to view memory usage and combine business logic to determine which data can be deleted. For example, some statistical data, or cached data that are not commonly used, can be considered cleaned.

To be more advanced, you can consider using the LRU (Least Recently Used) algorithm. Redis itself does not directly support LRU, but you can simulate LRU through some strategies, such as combining ZSET data structures, maintaining a sort of access time, and regularly cleaning up the data with the longest access time. This requires some programming skills, but it will be much more efficient than traversing KEYS directly.

There is another thing that is easy to overlook: the choice of data structures. Different data structures occupy different memory, such as String saves more memory than List . If you find that a key-value pair takes up too much memory, you can consider optimizing the data structure or using a more compact serialization method, such as ProtoBuf.

Of course, deleting data is not enough, and the problem must be solved from the root. It is important to monitor Redis's memory usage regularly. You can use Redis's own monitoring tools or some third-party monitoring tools to detect memory usage exceptions in a timely manner. Once the memory usage is found to be too high, timely measures should be taken to avoid the problem getting worse.

Finally, a very important experience: Don’t try untested solutions at will in production environments! Before performing any memory cleaning operations, be sure to make a backup and conduct sufficient testing in the test environment. Remember, data security is always the first priority.

Here is a simple Python script for deleting expired data (for reference only, it is not recommended to use directly in production environment):

 <code class="python">import redis r = redis.Redis(host='localhost', port=6379, db=0) while True: keys = r.keys('*') # 获取所有key for key in keys: if r.ttl(key) </code>

This script is just a simple example. In actual application, it needs to be adjusted according to your specific situation and add a more complete error handling and monitoring mechanism. Remember to use it with caution! Only by choosing the right strategy and combining monitoring can the problem of Redis full memory be effectively solved and avoid recurrence. Remember, prevention is better than treatment.

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