How does Redis deal with cache breakdown and cache avalanche issues?

How does Redis deal with cache breakdown and cache avalanche issues?

Redis is a commonly used cache database used to improve system performance and reduce the pressure on back-end databases. However, cache breakdown and cache avalanche problems can occur when data in the cache becomes invalid or is accessed simultaneously by a large number of requests.

1. Cache breakdown problem: When the cache of a hot data fails, a large number of requests will directly access the back-end database, causing excessive pressure on the database and affecting system performance. In order to solve the cache penetration problem, you can use the following methods:

   • Set the hotspot data to never expire: Set the hotspot data to never expire, so that even if the cache fails, the hotspot data can always be available.
   • Use a mutex lock: When the cache is invalid, use a mutex lock to ensure that only one thread can access the back-end database and cache the query results, while other threads wait for the results to be returned.

The following is a sample code that uses Java to operate Redis cache, including detailed comments:

import redis.clients.jedis.*;

public class RedisCacheExample {
    
    

    private Jedis jedis;

    public RedisCacheExample() {
    
    
        // 创建Jedis对象
        jedis = new Jedis("localhost");
    }

    public String get(String key) {
    
    
        // 从缓存中获取数据
        String value = jedis.get(key);

        if (value == null) {
    
    
            // 缓存失效，使用互斥锁保证只有一个线程能够访问后端数据库
            String lockKey = key + ":lock";
            String lockValue = UUID.randomUUID().toString();
            String result = jedis.set(lockKey, lockValue, "NX", "EX", 10);

            if (result != null && result.equals("OK")) {
    
    
                // 获取到锁，从后端数据库中查询数据
                value = queryFromDatabase(key);

                // 将查询结果存入缓存
                jedis.set(key, value);
                
                // 释放锁
                jedis.del(lockKey);
            } else {
    
    
                // 未获取到锁，等待其他线程返回结果
                try {
    
    
                    Thread.sleep(100);
                } catch (InterruptedException e) {
    
    
                    e.printStackTrace();
                }
                
                // 重新从缓存中获取数据
                value = jedis.get(key);
            }
        }

        return value;
    }

    private String queryFromDatabase(String key) {
    
    
        // 模拟从后端数据库查询数据
        return "value";
    }
}

The above sample code demonstrates how to use Java to operate the Redis cache and use a mutex lock to solve the cache breakdown problem. In the code, we first create a RedisCacheExampleclass that contains a getmethod for getting data from the cache.

In getthe method, we first get the data from the cache. If the data does not exist, the cache is invalid. Next, we use a mutex to ensure that only one thread can access the backend database. We set the lock's key name to key + ":lock", and generate a random lock value. Then, we use SETthe command to store the key-value pair of the lock in the cache, and set the expiration time to 10 seconds. We use NXparameters to indicate that the setting is successful only when the key does not exist.

If the lock is successfully obtained, we can query the data from the backend database and store the query results in the cache. Finally, we use DELthe command to release the lock. If the lock is not acquired, we wait for other threads to return results and re-fetch the data from the cache.

2. Cache avalanche problem: When a large amount of data in the cache fails at the same time, it will cause a large number of requests to directly access the back-end database, causing excessive pressure on the database and even causing database downtime. In order to solve the cache avalanche problem, you can use the following methods:

   • Set a random expiration time: Set the cache expiration time to a random value to prevent a large amount of data from invalidating at the same time.
   • Introduce multi-level cache: Introduce multi-level cache in the cache layer, such as using a combination of local cache and distributed cache to reduce direct access to the back-end database.
   • Data warm-up: During low system peak periods, hotspot data is loaded into the cache in advance to avoid direct access to the back-end database when the cache fails during peak periods.

To sum up, cache breakdown and cache avalanche are common problems in Redis, which can be solved by setting hot data to never expire, using mutex locks, setting random expiration times, introducing multi-level cache and data preheating, etc. . In practical applications, according to business needs and system scale, appropriate methods can be selected to deal with these problems and improve system performance and reliability.