Redis application scenarios are more and more diversified, and the strong scalability of Redis is being increasingly recognized. Combing through the dly operations of Redis applications, the expiration of Redis data is also particularly important to many users. In this paper, we will analyze some of the common clearance techniques for expired Redis collections.
From the actual operation point of view, most of the existing Redis data cleanup strategies can be generally classified into two categories: proactive expiration renewal and clearing expired data reheating.
For proactive expiration renewal, the basic idea is that deleting expired data records in a fixed time interval needs to periodically scan and clear the expired data records in Redis. We can use Redis-bgsave and system scheduling processes to achieve this purpose. For example, we can use the Linux system crontab command to achieve the effect of periodic execution of the date deletion task.
For the reheating of expired data, the core idea is to use a timer task to detect and delete Redis collections with expired data. Reheating of expired data has the essential feature in that it does not consume system resources when no expired data exists. The code implementation will look something like this:
// Define expiration time
long expire = 600000;
// Get all avlable Redis keys
Set keys = jedis.keys("*");
// Traverse all the keys
for (String key : keys) {
// Get the expiration time of each key
long expireTime = jedis.ttl(key);
// Judge whether the expiration time is exceeded, and delete the corresponding collection if it is exceeded
if (expireTime expire) {
jedis.del(key);
}
}
In short, the expiration of Redis collection data is inseparable from the dly use of Redis to a certn extent. In order to prevent excessive data from accumulating and affecting performance, it is necessary to take corresponding expiration strategies on the quantitative data, so as to ensure the normal use of the application.
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