How to deal with Redis cache avalanche?

Redis cache avalanche refers to the centralized failure of the cache, causing a large number of requests to be directly hit on the database, making the database unavailable. This situation usually occurs when the cache expiration time is not set properly or the cache capacity is insufficient.
In order to solve the Redis cache avalanche problem, the following measures can be taken:

1. Reasonably set the cache expiration time:

The cache expiration time can be set according to the business scenario and data update frequency to avoid simultaneous invalidation of a large number of caches. For example, you can use the Redis EXPIRE command to set the cache expiration time.

# 设置缓存过期时间，单位为秒  
EXPIRE key seconds  

2. Increase the cache capacity:

You can increase the cache capacity by increasing the number of cache nodes or increasing the capacity of a single node, so as to avoid cache avalanche. For example, you can use the Redis CONFIG command to set the cache size.

# 设置缓存容量，单位为字节  
CONFIG SET maxmemory-policy volatile-lru  
CONFIG SET maxmemory 100000000  

3. Optimize the database connection:

The database connection can be optimized, and the processing capacity of the database can be improved, so as to better cope with the cache avalanche. For example, you can use the MySQL CONNECT command to connect to the database.

# 连接 MySQL 数据库  
CONNECT mysql://username:password@localhost:3306/database  

Redis cache avalanche means that when the Redis cache fails centrally, a large number of requests will be sent to the database immediately, causing the database load to increase instantaneously, or even crash. In order to optimize the database connection, the following measures can be taken:

1. Introduce randomness: add a random value to the original cache expiration time to avoid a large amount of data from being invalidated at the same time. This reduces the risk of cache avalanche.
2. Set cache expiration time: Set cache expiration time according to data update frequency and access frequency. For data that is frequently updated, a shorter cache expiration time can be set, and for data with a lower access frequency, a longer cache expiration time can be set.
3. Use mutex: In the case of cache failure, the database can be queried only when the lock is obtained, which reduces the number of requests to access the database at the same time and prevents the database from crashing. However, mutexes may cause poor performance of the system.
4. Current limiting, circuit breaker and downgrade: Reduce server load by means of request current limit, circuit breaker mechanism, service downgrade, etc. This avoids cache avalanche.
5. Achieve high availability: By deploying multiple Redis instances and database instances, high availability of the cache and database is achieved. This can prevent a series of problems such as single point of failure, machine failure, and computer room downtime.
6. Improve database disaster recovery capability: improve database disaster recovery capability through data backup and master-slave switching. In this way, when the database crashes, it can quickly switch to the backup database to ensure the normal operation of the system.

4. Set cache warm-up:

You can set cache warm-up when the system starts, and load data into the cache in advance, thereby reducing the occurrence of cache avalanche. For example, you can use Redis's PING command to warm up the cache.

# 预热缓存  
PING key  

5. Use distributed cache:

You can use a distributed cache, such as Redis Cluster, to improve cache reliability and fault tolerance, thereby avoiding cache avalanche. For example, you can use Redis Cluster's CONFIG command to set up a distributed cache.

# 配置 Redis Cluster  
CONFIG SET cluster-enabled 1  
CONFIG SET cluster-config-file nodes.conf  

In practical applications, it is necessary to select an appropriate cache expiration time and capacity based on specific business scenarios and data update frequency to avoid cache avalanche. For example, for a frequently accessed page, the cache expiration time can be set to a shorter time, such as 30 seconds or 1 minute, while for a low-frequency accessed page, the cache expiration time can be set to a longer time, such as 1 hour or 1 day.
The following is a case analysis of Redis cache avalanche processing:
Suppose there is an e-commerce platform where users can purchase goods and the goods have corresponding inventory. When a user purchases an item, the inventory of the item needs to be updated. The following is an example code that uses Redis cache to handle item inventory:

# 初始化 Redis 连接  
redis_client = redis.StrictRedis(host='127.0.0.1', port=6379)
# 设置商品库存缓存过期时间为 1 分钟  
redis_client.expire('inventory:product:1', 60)
# 购买商品，更新库存  
def buy_product(product_id):  
   # 从缓存中读取商品库存  
   stock = redis_client.get('inventory:product:' + product_id)  
     
   # 如果缓存中没有商品库存，则更新库存并设置缓存过期时间  
   if stock is None:  
       new_stock = product_id * 2  # 假设库存为商品 ID 的两倍  
       redis_client.set('inventory:product:' + product_id, new_stock)  
       redis_client.expire('inventory:product:' + product_id, 60)  
   else:  
       # 否则，更新库存并设置缓存过期时间  
       new_stock = int(stock) - 1  
       redis_client.set('inventory:product:' + product_id, new_stock)  
       redis_client.expire('inventory:product:' + product_id, 60)
# 测试购买商品  
buy_product(1)  
buy_product(2)  
buy_product(3)  

In the above code, we first initialize the Redis connection and set the commodity inventory cache expiration time to 1 minute. Then, we define a buy_productfunction that buys the item and updates the inventory. In the function, we first read the product inventory from the cache, if there is no product inventory in the cache, update the inventory and set the cache expiration time. Otherwise, update the inventory and set the cache expiration time. Finally, we tested the case of purchasing an item.
In this example, we're assuming that the item inventory is twice the item ID. When a user purchases an item, we update the item inventory with the new value and set the cache expiration to 1 minute. In this way, when users purchase products continuously, the product inventory in the cache will be continuously updated, and the expiration time will be reset.
This is a simple example, and more factors may need to be considered in practical applications, such as database connection, transaction processing, and so on. In addition, the cache expiration time needs to be set according to the actual business scenario and data update frequency to avoid cache avalanche.