Using Redis to implement distributed cache penetration solution

Using Redis to implement distributed cache penetration solution

With the continuous development of Internet business, the amount of data access is also increasing. In order to improve the performance and User experience and caching technology have gradually become an indispensable part. Redis, as an efficient and scalable caching middleware solution, is favored by developers. When using Redis as a distributed cache, in order to avoid performance problems caused by cache penetration, we need to implement a reliable solution.

This article will introduce how to use Redis to implement a distributed cache penetration solution, and provide specific code examples to explain.

1. What is cache penetration?

When using caching technology, if strict validity control is not implemented on the cache, cache penetration problems may occur, that is, when the data required in a request does not exist in the cache, each time All requests will directly access the database, causing database resources to be overloaded, thereby reducing the performance of the entire system or even causing downtime.

The main reason for cache penetration is that all data cannot be stored in the cache, and the data in the request may not be stored in the cache. If there is no effective control, then each request will be directly Accessing the database causes an extreme waste of system resources.

2. How to solve the cache penetration problem

To solve the cache penetration problem, we can use the following two methods:

1. Bloom Filter algorithm

Bloom Filter algorithm is an efficient data structure based on bit vectors, which can be used to quickly determine whether an element belongs to a set. It has the characteristics of very low space and time complexity. When using the Bloom Filter algorithm, we can store the hash value of the requested data in the Bloom Filter's bit vector. If the hash value of the data request does not exist in the Bloom Filter, then the request can be directly rejected. This avoids the problem of cache penetration.

2. Cache preheating

Cache preheating refers to loading the data to be used into the cache in advance when the system starts to ensure that the request already exists before entering the background system in the cache, thereby avoiding cache penetration problems.

3. Using Redis to implement distributed cache penetration solution

When using Redis to implement distributed cache, we can use the following two methods:

1. Use Distributed lock

When making cache queries, we can use distributed locks to ensure that only one thread can access the database and update the cache. If multiple threads access the same data at the same time, only one thread can grab the lock, thus avoiding the problem of cache penetration.

The following is a code example implemented using distributed locks:

def query_data(key):
    #先尝试从缓存中读取数据
    data = cache.get(key)
    #如果缓存中没有该数据，则获取分布式锁
    if not data:
        lock_key = 'lock:' + key
        #尝试获取锁
        if cache.setnx(lock_key, 1):
            #若获取到锁，则从数据库中读取数据，并更新到缓存中
            data = db.query(key)
            cache.set(key, data)
            #释放锁
            cache.delete(lock_key)
        else:
            #如果未获取到锁，则等待一段时间后重试
            time.sleep(0.1)
            data = query_data(key)
    return data

2. Using Bloom filter

Before caching the query, we can first hash the data The hash value is stored in the Bloom filter. If the data corresponding to the hash value does not exist, the request can be directly rejected, thus avoiding the problem of cache penetration.

The following is a code example implemented using Bloom filters:

import redis
from pybloom_live import BloomFilter

#初始化布隆过滤器
bf = BloomFilter(capacity=1000000, error_rate=0.001)
#初始化Redis连接池
pool = redis.ConnectionPool(host='127.0.0.1', port=6379)
cache = redis.Redis(connection_pool=pool)

def query_data(key):
    #先尝试从缓存中读取数据
    data = cache.get(key)
    #如果缓存中没有该数据，则检查布隆过滤器，如果布隆过滤器中不存在该数据，则直接返回None
    if not data and (key not in bf):
        return None
    #如果缓存中没有该数据，但是存在于布隆过滤器中，则获取分布式锁
    if not data:
        lock_key = 'lock:' + key
        #尝试获取锁
        if cache.setnx(lock_key, 1):
            #若获取到锁，则从数据库中读取数据，并更新到缓存中
            data = db.query(key)
            cache.set(key, data)
            #将哈希值添加到布隆过滤器中
            bf.add(key)
            #释放锁
            cache.delete(lock_key)
        else:
            #如果未获取到锁，则等待一段时间后重试
            time.sleep(0.1)
            data = query_data(key)
    return data

The above is a specific implementation code example using Redis to implement a distributed cache penetration solution.

Summary:

When using Redis as a distributed cache middleware solution, in order to avoid performance problems caused by cache penetration, we can use distributed locks or bloom filters. method to solve. While using Bloom filters, we can also combine the cache preheating method to load the data to be used into the Redis cache in advance to ensure that the request already exists in the cache before entering the background system, thereby avoiding Cache penetration problem.

The above is the detailed content of Using Redis to implement distributed cache penetration solution. For more information, please follow other related articles on the PHP Chinese website!