Redis: quickly build a real-time statistics system

Redis (Remote Dictionary Server) is a memory-based data structure storage system that is lightweight, efficient, and easy to use. It is not only a high-speed key-value pair storage database, but also provides a variety of flexible data structures, such as strings, hashes, lists, sets, and ordered sets, which can support applications in various scenarios. In addition, Redis also has powerful real-time computing capabilities and can quickly build a real-time statistical system.

In practical application scenarios, it is often necessary to build a real-time statistical system. For example, e-commerce platforms need real-time statistics on sales data, operational data, and user data in order to optimize operational strategies. In this case, traditional relational databases can no longer meet real-time requirements, so Redis is widely used in the field of real-time computing.

This article will use code examples to introduce how to use Redis to build a simple real-time statistics system.

First, we need to store the data in Redis. Considering that we need to count the number of user visits, we can record the number of user visits in a set collection, where each element represents the access counter of each user.

import redis

r = redis.Redis(host='localhost', port=6379, db=0)

def record_user_access(user_id):
    r.sadd('users', user_id)
    r.incr('user:%s:access_count' % user_id)

In the above code, we use Redis's SADD command to add the user ID to a collection and use the INCR command to increment the user access counter. Next, we can use the Redis SCARD command to get the number of users and the SMEMBERS command to get the IDs of all users.

def get_user_count():
    user_count = r.scard('users')
    return user_count

def get_all_users():
    users = r.smembers('users')
    return users

Another common statistical method is to count the N users with the highest user visits. This can be done by using the ZADD command of Redis to add the user access counter as a score and the user ID as a member to an ordered set. .

def get_top_n_users(n):
    top_n = r.zrevrangebyscore('access_count', '+inf', '-inf', start=0, num=n)
    return top_n

def record_user_access(user_id):
    r.sadd('users', user_id)
    r.zincrby('access_count', user_id, amount=1)

Here we use the ZREVRANGEBYSCORE command of Redis to obtain the N users with the highest scores.

In addition to counting the number of user visits, we can also use Redis to count the number of page visits. Save the page's access counter in a Redis hash table, where the key is the page URL and the value is the access counter.

def record_page_view(url):
    r.hincrby('page_views', url, amount=1)

def get_page_view(url):
    page_view = r.hget('page_views', url)
    return page_view

In the above code, we use the HINCRBY command of Redis to increase the page counter, use the page URL as the key, and use the HGET command to obtain the access counter of the page when obtaining the number of visits.

In addition to the statistical methods introduced above, Redis also supports a variety of flexible data structures and commands to meet the needs of various scenarios. For example, if you need to count user behavior traces, you can use Redis's ordered collection to record user behavior logs, and use the ZREVRANGE command to obtain the user's recent behavior records.

In summary, Redis, as a memory-based data structure storage system, has the advantages of fast, efficient, and flexibility. It can not only be used as a high-speed key-value pair storage database, but also support a variety of flexible data structures and commands to meet the needs of various real-time computing scenarios.

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