###For help, training class e-commerce project. How to answer the actual questions of the interview project?##
1. When the activity is added to the shopping cart, the price is 8 yuan. After the event ends, it becomes 10 yuan. How do you deal with it?
2. After the event ends, the price of a large number of products needs to be revised, resulting in the accumulation of news, how to solve it?
In the face of large-scale price revisions causing news accumulation, I can offer the following solutions:
- Asynchronous processing : Design the price modification operation as an asynchronous task. After the event ends, put the product information that needs to be modified into the message queue, and then process these messages asynchronously through a multi-threaded or distributed processing system. This can reduce the real-time computing pressure during the activity and avoid the occurrence of message accumulation.
- Increase resource capacity: According to the actual situation, increase the processing capacity of the system, including increasing the number of servers, optimizing database performance, etc., so that a large number of price modification requests can be processed faster and the possibility of message accumulation can be reduced.
- Batch processing : The products are processed in batches according to certain rules, and the price modification requests of a part of the products are processed each time, instead of processing all the products at once. This can effectively control the risk of message accumulation and avoid overloading the system.
- Estimation and optimization : Estimate the modification of commodity prices during the event in advance, estimate the approximate amount of modification based on historical data and the scale of the event, and optimize system performance to meet high concurrency requirements and avoid message accumulation.
- Monitoring and alarm: Set up a monitoring system to monitor the length of the message queue, system load and other indicators in real time, and send an alarm in time when an abnormal situation occurs, so as to respond quickly and take corresponding countermeasures to prevent further deterioration of the message accumulation problem.
To sum up, the solutions provided above can help effectively deal with the problem of message accumulation caused by the price modification of a large number of commodities. The specific choice is related to the actual situation and the system architecture.
3. In the same queue, some messages are dedicated to modifying the price of the shopping cart, and some messages are for other things. It may be that the slowness of doing other things affects the task of updating the price. How should you deal with it? Those who execute slowly don't get stuck?
In response to this situation, the following solutions can be considered to avoid slow execution tasks from affecting price update tasks:
- Priority queue : Introduce the concept of priority in the message queue, set a higher priority for price update tasks, and ensure that they can be executed faster. In this way, even if there are slow tasks, it can ensure that the price update task will not be blocked.
- Asynchronous processing: For slow tasks, they can be designed as asynchronous tasks and processed by a separate thread or process, which can prevent them from blocking the price update task. You can use a thread pool or a task scheduler to manage and control the execution of tasks.
- Timeout mechanism : For tasks that execute slowly, you can set a reasonable timeout period. After the timeout is exceeded, it will be marked as failed or canceled, and the task of price update will continue. This avoids long blockages and ensures that price updates are completed in a timely manner.
- Task splitting : split a large task into multiple small tasks, and the execution time of each small task is relatively short, which can avoid blocking caused by excessive execution time of a single task. These small tasks can be executed concurrently using parallel computing or distributed computing. (For example, the IDM downloader downloader uses task splitting )
- Monitoring and alarm : Set up a monitoring system to monitor the execution of tasks and the queue length in real time. When the task execution time is too long or the queue length is abnormal, an alarm will be issued in time for timely processing and adjustment.
In practical applications, you can choose a suitable solution according to specific business scenarios and system requirements, or combine multiple solutions to deal with the impact of slow tasks on price updates.
4. For example, how to ensure that hot data is placed in redis instead of all data when there are activities?
Ensuring that hotspot data is put into Redis instead of all data can be achieved by:
- Data heat analysis : First, conduct heat analysis on the data to determine which data are frequently accessed hot data. Data access frequency and popularity indicators can be obtained through log analysis and business statistics.
- Cache strategy : According to the results of heat analysis, formulate a cache strategy to determine which data needs to be cached in Redis. Mark hot data as data that needs to be cached, while infrequently accessed data is excluded from the cache.
- Elimination strategy: When Redis memory resources are insufficient, an appropriate elimination strategy needs to be adopted to prioritize the elimination of infrequently accessed data to ensure the storage of hot data.
- Cache update strategy: For active scenarios, active update or delayed update strategies can be used to keep the cached data consistent with the data in the database. Active updates can update the cache immediately when activities are triggered or data changes, while delayed updates can update the cache through scheduled tasks to reduce frequent access to the database.
- Cache invalidation strategy: According to the life cycle of the activity and the frequency of data changes, set an appropriate cache invalidation time to keep the cached data valid during the activity and automatically expire after the activity ends to avoid data expiration or unnecessary data retention.
It is necessary to determine the selection and caching strategy of hot data according to specific business scenarios and requirements, while comprehensively considering system performance, memory resources, and data consistency.
5. What are the advantages of using mq to cancel orders over time?
6. If the user clicks to pay just one second before closing the order, can the payment still be successful? How to deal with it?
7. The order has just been paid but just timed out and entered the dead letter queue, what should I do?
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