Key points in processing push messages and real-time recommendations in the PHP flash sale system

Title: Push messages and real-time recommendation processing points in PHP flash sale system

Article text:

With the booming development of e-commerce, flash sales Events are being adopted by more and more platforms, not only to increase sales but also to attract more users to participate. However, in high concurrency situations, traditional systems often cannot meet the real-time and stability requirements of flash sale activities. In order to solve this problem, push messages and real-time recommendation processing in the PHP flash sale system are particularly important.

1. Key points of push message processing

1. Use message queue: During flash sale activities, a large number of requests are initiated at the same time. If the database is directly operated, it will often cause excessive database access pressure. large, causing the system to crash. Therefore, you can use the message queue for asynchronous processing of messages, add the request to the message queue, and then let the background consumer process the message.
2. Use cache: In order to improve the concurrent processing capability of the system, you can use cache to temporarily store flash sale requests to reduce the load on the database. You can use caching tools such as Redis or Memcached to store requests in the cache, set an expiration time, and then write the data to the database when it expires.
3. Concurrency control: In flash sale activities, due to the large number of participants, if concurrency is not controlled, it can easily cause overselling and other problems. You can judge the purchase quantity or use distributed locks for concurrency control to ensure that each user can only purchase once.

2. Key points of real-time recommendation processing

1. User preference analysis: In order to achieve real-time recommendation, it is first necessary to conduct personalized preference analysis of users. The user's interests and preferences can be judged through the user's browsing history, purchase history and other information, and corresponding recommendations can be made.
2. Use recommendation algorithm: In the PHP flash sale system, you can use the recommendation algorithm to make real-time recommendations. Common recommendation algorithms include collaborative filtering, content-based recommendation, etc. By analyzing user behavior data, the correlation between users and products can be obtained, and then real-time recommendations can be made based on the correlation.
3. Cache preheating: In order to improve the efficiency of recommendation, the recommendation results can be cached in the cache and preheated. When the system starts, the recommendation results of popular products can be loaded in advance to reduce the amount of calculation required each time a recommendation is requested.

Code example:

The following is a code example for push message and real-time recommendation processing in a simple PHP flash sale system:

// 使用消息队列实现推送消息处理
$messageQueue = new MessageQueue();
$messageQueue->pushMessage($message);

// 使用缓存存储秒杀请求
$cache = new RedisCache();
$cacheKey = "seckill:request:$userId";
$cache->set($cacheKey, $request, $expiration);

// 并发控制
$lock = new DistributedLock($productId);
if ($lock->lock()) {
    // 处理秒杀请求
    $seckillService->processSeckill($productId, $userId);
    $lock->unlock();
}

// 用户偏好分析
$preferenceAnalyzer = new PreferenceAnalyzer();
$preferenceAnalyzer->analyze($userId);

// 使用推荐算法进行实时推荐
$recommendationEngine = new RecommendationEngine();
$recommendation = $recommendationEngine->getRecommendation($userId);

// 缓存预热
$cache = new RedisCache();
$cacheKey = "recommendation:$userId";
if (!$cache->has($cacheKey)) {
    $cache->set($cacheKey, $recommendation, $expiration);
}

The above code example is for reference only , in actual applications, it needs to be adapted and optimized according to specific conditions.

Summary:

In the PHP flash sale system, push message processing and real-time recommendation processing are important links to ensure the real-time and stability of the system. By rationally using technical means such as message queues, caching, and recommendation algorithms, the system's concurrent processing capabilities and user experience can be effectively improved.

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